Proc Arima Gleitender Durchschnitt

Beliebte Themen in diesem Abschnitt sind die Verwendung von PROC REPORT, SAS Styles, Templates und ODS sowie eine Vielzahl von Techniken, die zur Erstellung von SAS-Ergebnissen in Microsoft Excel, Powerpoint und anderen Office-Anwendungen verwendet werden. Themen sind Grafiken, Datenvisualisierung, Publishing und Reporting. Beliebte Themen in diesem Abschnitt sind die Verwendung von SASGraph, SAS Styles, Templates und ODS sowie eine Vielzahl von Techniken verwendet, um SAS Ergebnisse in Microsoft Excel und andere Office-Anwendungen zu produzieren. Die Datenwissenschaft gilt als eine Erweiterung von Statistiken, Data Mining und Predictive Analytics. Dieser Abschnitt konzentriert sich darauf, wie quotthe Sexiest Job des 21. Centuryquot in SAS durchgeführt wird. Interessensgebiete sind Textanalyse und Social Media-Daten. Moderatoren bereiten eine Digitalanzeige vor, die von allen Teilnehmern während der gesamten Konferenz zur Verfügung stehen wird, anstatt eine Präsentation im Vortragsstil durchzuführen. Der Abschnitt zeigt oft hochauflösende Grafiken und oder Denkanstöße Konzepte oder Ideen, die eine unabhängige Studie von Konferenzteilnehmern ermöglichen. Präsentationen konzentrieren sich auf die Visualisierung von Daten, darunter PROC GPLOT, animierte Grafiken und andere Anpassungen. Hands-on-Workshops bieten den Teilnehmern lsquohands-on-the-keyboardrsquo Interaktion mit SAS Software während jeder Präsentation. Presenter führen Teilnehmer durch Beispiele von SAS Software-Techniken und Fähigkeiten, während die Möglichkeit, Fragen zu stellen und durch Praxis zu lernen. Alle HOW-Präsentationen werden von erfahrenen SAS-Nutzern gegeben, die zur Präsentation eingeladen werden. Dieser Abschnitt enthält Präsentationen zur Datenintegration, - analyse und - berichterstattung, jedoch mit branchenspezifischen Inhalten. Beispiele für inhaltsgesteuerte Themen sind: Health Outcomes und Healthcare Forschungsmethoden Datenstandards und Qualitätskontrolle für die Einreichung von klinischen Studien zu FDA Banking, Kreditkarten-, Versicherungs - und Risikomanagement Versicherungsmodellierung und - analyse Dieser Abschnitt hilft SAS-Anwendern, Die reiche Welt der Ressourcen, die für die Erreichung qualitativ hochwertiger SAS Educationtraining, Publikation, soziale Netzwerke, Beratung, Zertifizierung, technische Unterstützung und Möglichkeiten für berufliche Zugehörigkeit und Wachstum gewidmet sind. Dieser Abschnitt ermöglicht es Novice SAS-Benutzern und anderen, an einer Reihe von Präsentationen teilzunehmen, die sie durch die grundlegenden Konzepte der Erstellung des Basis-SAS-DATA-Schrittes und der PROC-Syntax führen, gefolgt von zwei Hands-on Workshops. Alle SAS Essentials-Präsentationen werden von erfahrenen SAS-Nutzern durchgeführt, die zur Präsentation eingeladen werden. Wenn Sie ein Programm haben, das eine lange Zeit läuft oder mehrere Male ausgeführt wird, sollten Sie die Länge der einzelnen Teile des Programms verfolgen. Dies kann Ihnen helfen, die langsamen Teile Ihres Programms zu finden und vorherzusagen, wie lange ein zukünftiger Lauf dauert. Dieses Papier präsentiert ein Werkzeug, um mit diesen Problemen zu helfen. Das WriteProgramStatus-Makro bietet eine Möglichkeit, eine Statusdatei zu erstellen, die leicht von Menschen oder Maschinen gelesen werden kann. Beyond IF THEN ELSE: Techniken zur bedingten Ausführung von SAS-Code Fast jedes SASreg-Programm enthält Logik, die bewirkt, dass bestimmte Code nur ausgeführt werden, wenn bestimmte Bedingungen erfüllt sind. Dies geschieht häufig mit dem IF. DANN. ELSE-Syntax. In diesem Beitrag werden wir verschiedene Wege zur Konstruktion einer bedingten SAS-Logik erforschen, einschließlich solcher, die Vorteile gegenüber der IF-Anweisung bieten können. Zu den Themen gehören die SELECT-Anweisung, die IFC - und IFN-Funktionen, die CHOOSE - und WHICH-Funktionsfamilien sowie die COALESCE-Funktion. Wersquoll stellen auch sicher, dass wir den Unterschied zwischen einer regulären IF und der IF-Makro-Anweisung verstehen. Eine Waze-App für Base SASreg: Automatisches Routing um gesperrte Datensätze, Bottleneck-Prozesse und andere Verkehrsstörungen auf den Daten-Superhighwa Die Waze-Anwendung, die von Google im Jahr 2013 gekauft wurde, warnt Millionen von Nutzern über Verkehrsstaus, Kollisionen, Bauarbeiten und andere Komplexitäten der Straße, die stymie Autofahrer versuchen, von A bis B. Von jackknifed Rigs zu Jackalope Kadaver können Straßen knirscht werden durch Gridlock oder mit Hindernisse, die Verkehr und Effizienz behindern gestört werden. Waze-Algorithmen automatisch umleiten Benutzer zu effizienteren Routen auf der Grundlage von benutzerdefinierten Ereignissen sowie historische Normen, die typische Straßenbedingungen zu demonstrieren. Extrahieren, Transformieren, Laden (ETL) - Infrastrukturen stellen oft serialisierte Prozessabläufe dar, die Autobahnen nachahmen können, und die durch verriegelte Datensätze, langsame Prozesse und andere Faktoren, die Ineffizienz einführen, ähnlich verknotet werden können. Das LOCKITDOWN SASreg-Makro, das 2014 bei WUSS eingeführt wurde, erkennt und verhindert Datenzugriffskollisionen, die auftreten, wenn zwei oder mehr SAS-Prozesse oder Benutzer gleichzeitig versuchen, auf denselben SAS-Datensatz zuzugreifen. Darüber hinaus bietet das LOCKANDTRACK-Makro, eingeführt bei WUSS im Jahr 2015, Echtzeit-Tracking und historische Performance-Metriken für gesperrte Datensätze durch eine einheitliche Steuertabelle und ermöglicht Entwicklern, Prozesse zu optimieren, um Effizienz und Datendurchsatz zu optimieren. Dieser Text veranschaulicht die Implementierung von LOCKSMART und seiner Sperrleistungsmetriken, um datengetriebene Fuzzy-Logikalgorithmen zu erzeugen, die den Programmablauf präventiv um nicht zugreifbare Datensätze umleiten. Somit erwartet die Software, anstatt unnötig darauf zu warten, dass ein Datensatz verfügbar wird oder ein Prozess abzuschließen, die Wartezeit auf der Grundlage historischer Normen, führt andere (unabhängige) Funktionen aus und kehrt zum ursprünglichen Prozess zurück, wenn sie verfügbar ist. Die Notfallmedizin besteht aus einem Kontinuum von Sorgfalt, die oft mit Erster Hilfe, Grundlebensunterstützung (BLS) oder fortgeschrittener Lebensunterstützung begonnen wird (ALS). Zuerst wird die Notfallmedizin in das 21. Jahrhundert eingeführt Responder, einschließlich Feuerwehrleute, Notfallmediziner (EMTs) und Sanitäter, sind oft die ersten, die Kranke, Verletzte und Kranke testen, die Situation rasch einschätzen, kurative und palliative Betreuung anbieten und Patienten in medizinische Einrichtungen transportieren. Notfallmedizinische Behandlungsprotokolle und Standard-Operationsverfahren (SOPs) sorgen dafür, dass geschultes Personal trotz der Einzigartigkeit eines jeden Patienten sowie potentieller Komplikationen eine Reihe von Werkzeugen und Techniken zur Verfügung stellt, um unterschiedliches Maß an Sorgfalt in einem standardisierten, Wiederholbar und verantwortungsbewusst. So wie EMS-Anbieter die Patienten beurteilen müssen, um eine wirksame Vorgehensweise vorzuschreiben, sollte Software auch Prozessabweichungen oder - versagen identifizieren und beurteilen und in ähnlicher Weise deren angemessenes Vorgehen vorschreiben. Die Ausnahmebehandlung beschreibt die Identifikation und die Auflösung von unerwarteten oder unerwarteten Ereignissen, die während der Softwareausführung auftreten können, und sollte in SASreg-Software implementiert werden, die Zuverlässigkeit und Robustheit verlangt. Das Ziel der Ausnahmebehandlung ist immer, die Prozesssteuerung zurück zu dem quothappy trailquot oder quothappy pathquotmdashi. e umzuleiten. Der ursprünglich beabsichtigte Prozesspfad, der vollen Geschäftswert liefert. Wenn jedoch unüberwindbare Ereignisse auftreten, sollten Ausnahmeverarbeitungsroutinen den Prozess, das Programm oder die Sitzung anmutig beenden, um Schäden oder andere unerwünschte Effekte zu vermeiden. Zwischen den entgegengesetzten Ergebnissen eines vollständig wiederhergestellten Programms und der anmutigen Programmbeendigung liegen jedoch einige andere Ausnahmemöglichungspfade, die vollen oder teilweisen Geschäftswert liefern können, manchmal nur mit einer leichten Verzögerung. Zu diesem Zweck veranschaulicht dieser Text diese Wege und diskutiert verschiedene interne und externe Modalitäten für die Kommunikation von Ausnahmen von SAS-Benutzern, Entwicklern und anderen Stakeholdern. Wouldnrsquot es nett sein, wenn Ihr lang-laufendes Programm Sie auf die Schulter klopfen könnte und sagen lsquoOkay, Irsquom alle getan nowrsquo. Es kann Dieser schnelle Tipp zeigt Ihnen, wie einfach es ist, Ihr SASreg Programm senden Sie (oder jemand anderes) eine E-Mail während der Ausführung des Programms. Sobald yoursquove die einfachen Grundlagen unten erhielt, kommen herauf yoursquoll mit allen möglichen Verwendungen für diese große Eigenschaft und yoursquoll wundern, wie Sie überhaupt ohne es lebten. Finden Sie alle Unterschiede in zwei SAS-Bibliotheken mit Proc Vergleichen Bharat Kumar Janapala In der klinischen Industrie Validierung der Datensätze durch parallele Programmierung und proc Vergleich dieser abgeleiteten Datensätze ist eine Routine-Praxis, aber aufgrund der ständigen Updates in Rohdaten wird es schwierig, herauszufinden, Unterschiede zwischen Zwei Bibliotheken. Das aktuelle Programm zeigt mit Hilfe von Proc-Vergleichs - und SAS-Hilfeverzeichnissen alle Unterschiede zwischen den Bibliotheken optimal an. Zunächst ermittelt das Programm die in den Bibliotheken vorhandenen Datensätze und listet die ungewöhnlichen Datensätze auf. Zweitens sucht das Programm nach der Gesamtzahl der Beobachtungen und Variablen, die in beiden Bibliotheken nach Datensatz vorhanden sind, und listet sowohl die ungewöhnlichen Variablen als auch die Datensätze mit Unterschieden in der Beobachtungsnummer auf. Drittens, unter der Annahme, dass beide Bibliotheken identisch sind, vergleicht das Programm proc die Datasets mit ähnlichen Namen und erfasst die Unterschiede, die überwacht werden könnten, indem die maximale Anzahl von Unterschieden durch die Variable zur Optimierung zugewiesen wird. Schließlich liest das Programm alle Unterschiede und liefert einen konsolidierten Bericht gefolgt von der Beschreibung durch Datensatz. Lassen Sie die Umgebungsvariable Ihnen helfen: Verschieben von Dateien über Studien und Erstellen von SAS-Bibliothek On-The-Go In klinischen Studien werden Datensätze und SAS-Programme unter verschiedenen Studien unter verschiedenen Produkten in Unix gespeichert. SAS-Programmierer müssen häufig auf diese Standorte zugreifen, Daten für die Programmierung einlesen oder Dateien zur Wiederverwendung in einer neuen Analyse kopieren. Das Schreiben des langen Verzeichnispfads ist sehr zeitraubend und nervenaufreibend. Dieses Papier beschreibt eine effiziente Möglichkeit, die verschiedenen Verzeichnispfade im Voraus durch Umgebungsvariablen zu speichern. Diese vordefinierten Umgebungsvariablen können für Unix-Dateivorgänge (Kopieren, Löschen, Suchen nach Dateien usw.) verwendet werden. Informationen, die von diesen Variablen getragen werden, können auch in SAS übertragen werden, um Bibliotheken zu erstellen, wohin Sie gehen. Überprüfen Sie bitte: Eine automatisierte Annäherung zum Protokollüberprüfung In der pharmazeutischen Industrie finden wir uns, unsere Programme wiederholt für jedes deliverable wieder laufen zu lassen. Diese Programme können einzeln in einer interaktiven SASreg-Sitzung ausgeführt werden, die es uns ermöglicht, die Protokolle zu überprüfen, während wir die Programme ausführen. Wir konnten das einzelne Programm im Batch ausführen und jedes einzelne Protokoll öffnen, um auf unerwünschte Log-Meldungen wie ERROR, WARNING, uninitialisiert, usw. zurückzugreifen. Beide Ansätze sind gut, wenn es nur eine Handvoll Programme gibt ausführen. Aber was tun Sie, wenn Sie Hunderte von Programmen haben, die erneut ausgeführt werden sollen Möchten Sie jedes einzelne der Programme öffnen und nach unerwünschten Nachrichten suchen Dieser manuelle Ansatz kann Stunden dauern und ist anfällig für versehentliches Versehen. In diesem Artikel wird ein Makro besprochen, das ein bestimmtes Verzeichnis durchsucht und entweder alle Protokolle im Verzeichnis überprüft oder nur Protokolle mit einer bestimmten Namenskonvention überprüft oder nur die aufgelisteten Dateien überprüft. Das Makro erzeugt dann einen Bericht, der alle aufgelisteten Dateien auflistet und anzeigt, ob Probleme gefunden wurden oder nicht. Lassen Sie SASreg Ihre DIRty Arbeit sicherstellen, dass Sie alle notwendigen Informationen haben, um ein deliverable gespeichert zu replizieren kann eine umständliche Aufgabe sein. Sie wollen sicherstellen, dass alle Rohdatensätze gespeichert sind, werden alle abgeleiteten Datensätze, egal ob es sich um SDTM - oder ADaM-Datensätze handelt, gespeichert und Sie bevorzugen, dass die datetime Stempel beibehalten werden. Nicht nur benötigen Sie die Datensätze, müssen Sie auch eine Kopie aller Programme, die verwendet wurden, um die lieferbaren produzieren sowie die entsprechenden Protokolle, wenn die Programme ausgeführt wurden zu halten. Alle anderen Informationen, die benötigt wurden, um die notwendigen Ausgänge zu erzeugen, müssen gespeichert werden. All dies muss für jede Lieferung durchgeführt werden und es kann leicht sein, einen Schritt oder einige wichtige Informationen zu übersehen. Die meisten Leute tun diesen Prozess manuell und es kann ein zeitaufwändiger Prozess sein, warum also nicht lassen SAS die Arbeit für Sie tun LST Dateien mit Proc Vergleich Ergebnis Manvitha Yennam und Srinivas Vanam Die am weitesten verbreitete Methode zur Validierung von Programmen ist Double Programming, die Umfasst zwei Programmierer, die an einem einzigen Programm arbeiten und schließlich ihre Ausgänge anhand von Prozeduren wie ldquocomparerdquo vergleichen. Die Proc Compare-Ergebnisse werden in der Regel in. LST-Dateien produziert. Die meisten Unternehmen tun die manuelle Überprüfung, indem sie jede einzelne. LST-Datei, um sicherzustellen, dass die Ausgaben ähnlich sind. Aber dieser manuelle Prozess ist zeitaufwändig sowie fehleranfällig. Der Zweck dieser Arbeit ist es, ein SAS-Makro zu verwenden, anstatt den manuellen Überprüfungsprozess zu befolgen. Dieses SAS-Makro liest alle. LST-Dateien ein Pfad und erstellt eine Zusammenfassung der Listen-Dateien und zeigt, ob es Problem oder nicht und auch die Art des Problems. Lesen Sie jede Publikation von nationalen Medien zu Ihrer lokalen Nachrichtenweb site. Pädagogische Leistung, vor allem in STEM-Felder, ist ein schweres Anliegen und Milliarden von Dollar ausgegeben werden, um diese Frage. Wie kann SAS angewendet werden, um das Ergebnis einer Intervention zu analysieren und, gleichermaßen wichtig, die Ergebnisse dieser Analyse an ein nicht-technisches Publikum zu verteilen. Mit realen Daten aus Evaluationen von Bildungsspielen geht diese Präsentation durch die Schritte einer Evaluation, von Bedarfsermittlung bis hin zur Validierung von Messungen vor dem Testvergleich. Die angewandten Techniken umfassen PROC FREQ mit Optionen für korrelierte Daten, PROC FACTOR für Faktorenanalyse, PROC TTEST und PROC GLM für wiederholte Maßnahmen ANOVA. Glücklicher Gebrauch wird während der gesamten ODS statistischen Grafiken gebildet. Mit Standard-SASSTAT-Prozeduren können diese Analysen auf jedem beliebigen Betriebssystem mit SAS ausgeführt werden, einschließlich SAS Studio auf einem iPad. Konstruieren von Konfidenzintervallen für Differenzen von Binomialproportionen in SASreg Angesichts zweier binomischer Proportionen wollen wir ein Konfidenzintervall für die Differenz erstellen. Die am weitesten verbreitete Methode ist die Waldmethode (dh normale Näherung), sie kann jedoch im Extremfall zu unerwünschten Ergebnissen führen (zB wenn die Proportionen nahe 0 oder 1 liegen). Es gibt zahlreiche andere Methoden, darunter asymptotische Methoden, approximative Methoden und exakte Methoden. Dieses Papier präsentiert 9 verschiedene Methoden für den Aufbau solcher Konfidenzintervalle, von denen 8 in SASreg 9.3 Verfahren zur Verfügung stehen. Die Methoden werden verglichen und Gedanken über die zu verwendende Methode gegeben. Eine animierte Anleitung: Incremental Response Modelling in Enterprise Miner Einige Leute können erwartet werden, ein Produkt ohne Marketing-Kontakt zu kaufen. Wenn alle potenziellen Kunden kontaktiert werden, kann ein Unternehmen nicht bestimmen, die wahre Wirkung einer Marketing-Manipulation. Dieses Gespräch verwendet den Knoten INCREMENTAL RESPONSE in SASreg Enterprise Minertrade, um ein grundlegendes Marketingproblem zu lösen. Marketers in der Regel Ziel, und verbringen Sie Geld kontaktieren, alle potenziellen Kunden. Das ist verschwenderisch, denn einige dieser Leute würden auf eigene Faust Kunden werden. Dieser Knoten nutzt eine Reihe von Daten, um Kunden in Gruppen zu trennen: 1) wahrscheinlich zu kaufen 2) wahrscheinlich zu kaufen, wenn sie ein Thema von Marketing-Kampagnen und 3) Kunden, die erwartet werden, resistent gegen Marketing-Bemühungen. Arbeiten mit latenten Analysen in Längsschnittstudien: Eine Erforschung von unabhängig entwickelten SASreg-Prozeduren Dieses Papier untersucht mehrere Möglichkeiten, um latente Variablen in Längsschnittuntersuchungen zu untersuchen, indem drei unabhängige SASreg-Verfahren verwendet werden. Drei verschiedene Analysen für die latente Variable-Entdeckung werden untersucht und erforscht: Latent-Class-Analyse, latente Transitionsanalyse und latente Trajektorienanalyse. Die in diesem Papier untersuchten latenten Analyseverfahren (die alle außerhalb des SASreg-Instituts entwickelt wurden) sind PROC LCA, PROC LTA und PROC TRAJ. Die Besonderheiten hinter diesen Verfahren und wie sie zu Onersquos Verfahren Bibliothek hinzugefügt werden, werden untersucht und dann auf eine explorative Fallstudie Frage angewendet werden. Die Wirkung der latenten Variablen auf die Anpassung und Verwendung des Regressionsmodells im Vergleich zu einem ähnlichen Modell unter Verwendung von beobachteten Daten kann auch kurz untersucht werden. Die Daten für diese Studie verwendet wurde, über die National Longitudinal Study of Adolescent Health, eine Studie verteilt und gesammelt von Add Gesundheit. Die Daten wurden mit SAS 9.4 analysiert. Dieses Papier ist für moderate bis fortgeschrittene SASreg Benutzer gedacht. Dieses Papier ist auch geschrieben, um ein Publikum mit einem Hintergrund in Verhaltensforschung andor Statistiken. MIghty PROC MI zur Rettung Fehlende Daten sind ein Merkmal vieler Datensätze, da sich die Teilnehmer aus Studien zurückziehen, keine selbst gemeldeten Maßnahmen vornehmen können und manchmal technische Probleme die Datenerfassung beeinträchtigen können. Wenn wir nur vollständige Beobachtungen verwenden, sind wir mit größeren Standardfehlern, größeren Konfidenzintervallen und größeren p-Werten belassen. Fehlende Datenverfahren wie vollständige Fallanalyse oder Imputation können verwendet werden, aber die fehlenden Datenmechanismen und - muster müssen zuerst verstanden werden. Dieses Papier gibt einen Überblick über fehlende Datenquellen, Muster und Mechanismen. Ein vollständiger Datensatz wird verwendet, um echte Regressionsanalyseergebnisse zu erhalten. Es werden zwei Datensätze mit fehlenden Werten erstellt, eine mit fehlenden Daten zufällig und eine mit fehlenden Daten nicht zufällig. Die fehlenden Datenmethoden des kompletten Falles, einzelner und mehrfacher Imputation werden angewendet. Proc MI und MIANALYZE werden in SASreg 9.4 für die Analyse verwendet. Die Ergebnisse der fehlenden Datenmethoden werden miteinander und mit den wahren Ergebnissen verglichen. John Amrhein und Fei Wang Motiviert durch die häufige Notwendigkeit für Äquivalenztests in klinischen Studien, bietet dieses Papier Einblicke in Tests für Äquivalenz. Wir fassen zusammen und vergleichen Äquivalenztests für verschiedene Studienentwürfe, einschließlich Entwürfe für ein Musterproblem, Entwürfe für Zweiprobenproblem (gepaarte Beobachtungen und zwei unabhängige Proben) und Entwürfe mit mehreren Behandlungsarmen. Leistung und Stichprobengrößenschätzung werden diskutiert. Wir geben auch Beispiele für die Implementierung der Methoden mit den Prozeduren FREQ, TTEST, MIXED und POWER in SASSATreg Software. Abstandskorrelation für Vektoren: Ein SASreg-Makro Der Pearson-Korrelationskoeffizient ist gut bekannt und weit verbreitet. Allerdings leidet es unter bestimmten Einschränkungen: Es ist ein Maß für die lineare Abhängigkeit (nur) und liefert keinen Test der statistischen Unabhängigkeit, und es ist auf univariate Zufallsvariablen beschränkt. Seit ihrer Gründung wurden verwandte und alternative Maßnahmen vorgeschlagen, um diese Einschränkungen zu überwinden. Mehrere neue Maßnahmen, die Pearson-Korrelation zu ersetzen oder zu ergänzen, wurden in der statistischen Literatur in den letzten Jahren vorgeschlagen. Szekeley et al. (2007) beschreibt eine neue Maßnahme - Abstandskorrelation -, die die Defizite der Pearson-Korrelation überwindet. Die Distanzkorrelation wird für 2 Zufallsvariablen X, Y (die Vektoren sein können) als Gewichts - oder Distanzfunktion definiert, die auf die Differenz zwischen der gemeinsamen charakteristischen Funktion für (X, Y) und dem Produkt der einzelnen charakteristischen Funktionen für X, Y angewendet wird In der Praxis wird sie durch Berechnung der einzelnen Entfernungsmatrizen für X, Y geschätzt, und die Abstandskorrelation ist ein Ähnlichkeitsmaß für die 2 Matrizen. Für den bivariaten Normalfall ist die Distanzkorrelation eine Funktion der Pearson-Korrelation. Die Distanzkorrelation unterstützt auch einen entsprechenden Test der statistischen Unabhängigkeit. Abstandskorrelation hat gut in den Simulationsstudien durchgeführt, die es mit anderen Alternativen zu Pearson-Korrelation vergleichen. Hier präsentieren wir eine Base SASreg Makro zu berechnen Distanz Korrelation für beliebige reelle Vektoren. Bestimmung der Funktionalität von Wasserpumpen in Tansania mit SASreg EM und VA Indien Kiran Chowdaravarpu, Vivek Manikandan Damodaran und Ram Prasad Poudel Der Zugang zu sauberem und hygienischem Trinkwasser ist ein grundlegender Luxus, den jeder Mensch verdient. In Tansania gibt es 23 Millionen Menschen, die keinen Zugang zu sicherem Wasser haben und gezwungen sind, Meilen zu gehen, um Wasser für den täglichen Bedarf zu holen. Das vorherrschende Problem ist eher ein Ergebnis einer schlechten Instandhaltung und eines ineffizienten Funktionierens bestehender Infrastrukturen wie Handpumpen. Um die gegenwärtige Wasser-Krise zu lösen und die Zugänglichkeit zu sicherem Wasser sicherzustellen, besteht die Notwendigkeit, nicht funktionale und funktionelle Pumpen zu lokalisieren, die repariert werden müssen, damit sie repariert oder ersetzt werden können. Es ist sehr kostenintensiv und unpraktisch, die Funktionalität von über 74.251 Wasserpunkten manuell in einem Land wie Tansania, wo die Ressourcen sehr begrenzt sind zu überprüfen. Das Ziel dieser Studie ist es, ein Modell zu bauen, um zu prognostizieren, welche Pumpen funktional sind, die einige Reparatur und die donrsquot Arbeit mit den Daten aus dem Tansania Ministerium für Wasser braucht. Wir finden auch die wichtigen Variablen, die die pumprsquos Arbeitsbedingung vorherzusagen. Die Daten werden vom Taarifa waterpoints Armaturenbrett verwaltet. Nach der Vorverarbeitung bestehen die Enddaten aus 39 Variablen und 74.251 Beobachtungen. Wir verwendeten SAS Bridge für ESRI und SAS VA, um die räumliche Variation der funktionalen Wasserpunkte auf regionaler Ebene von Tansania zusammen mit anderen sozioökonomischen Variablen zu illustrieren. Unter dem Entscheidungsbaum, dem neuronalen Netzwerk, der logistischen Regression und den HPrandom-Waldmodellen wurde das Modell HP Random Forest als das beste Modell gefunden. Die Fehlklassifizierungsrate, Sensitivität und Spezifität des Modells betragen 24,91, 62,7 bzw. 91,7. Die Klassifizierung der Wasserpumpen mit dem Champion-Modell beschleunigt Wartungsarbeiten der Wasserpunkte, die sauberes und zugängliches Wasser über Tansania in niedrigen Kosten und in einer kurzen Zeitspanne sicherstellen. Anpassung Threshold-Modelle mit dem SASreg Verfahren NLIN und NLMIXED Hierarchische verallgemeinerte lineare Modelle für Behavioral Health Risk-Standardisierte 30-Tage-und 90-Tage-Rückführungsraten Die Leistungen in Clinical Excellence (ACE) - Programm ermutigt Exzellenz über alle Verhaltensregeln Netzwerk-Einrichtungen durch die Förderung derjenigen, Bieten die höchste Qualität der Pflege. Zwei wichtige Maßstäbe für die Effektivität der Wirksamkeit des ACE-Programms sind die risikoadjustierten 30-tägigen Rückübernahme - und risikoadaptierten 90-Tage-Rückübernahmeraten. Die Risikokorrektur wurde mit hierarchischen allgemeinen linearen Modellen (HGLM) durchgeführt, um die Unterschiede zwischen den Krankenhäusern bei den demographischen und klinischen Merkmalen des Patienten zu berücksichtigen. Ein Jahr der administrativen Zulassungsdaten (30. Juni 2013 bis 1. Juli 2014) von Patienten für 30 Tage (N78.761, N Krankenhäuser2.233) und 90-Tage (N74.540, N Krankenhaus 2.205) Zeitrahmen waren die Datenquellen. HGLM gleichzeitig Modelle zwei Ebenen 1) Patientenniveau ndash Modelle Log-Chancen der Krankenhaus-Rückübernahme mit dem Alter, Geschlecht, ausgewählten klinischen Kovariaten und einem krankenhausspezifischen Intercept und 2) Krankenhaus Ebene ndash eine zufällige Krankenhaus-Intercept, dass Konten für innere Krankenhaus-Korrelation Der beobachteten. PROC GLIMMIX wurde verwendet, um eine HGLM mit Krankenhaus als eine zufällige (hierarchische) Variable separat für Substanzgebrauch Disorder (SUD) Zulassungen und psychische Gesundheit (MH) Zulassungen zu implementieren und gepoolt, um eine krankenhausweite Risk Adaption Rate erhalten. Die HGLM-Methodik wurde aus CMS-Dokumentationen für die 2013 Hospital-Wide All-Cause Risk-Standardized Readmission Measure SAS-Paket abgeleitet. Diese Methodik wurde separat auf 30-Tage - und 90-Tage-Rückmeldedaten durchgeführt. Die endgültigen Metriken waren ein krankenhausweites Risiko angepasst 30-Tage-Rücklaufquote Prozent und ein Krankenhaus-weiten Risiko angepasst 90-Tage-Rücklaufquote Prozentsatz. HGLM-Modelle wurden auf neuen Produktionsdaten, die mit der Entwicklungsstichprobe überlappten, validiert. Überarbeitete HGLM-Modelle wurden im April 2015 getestet, und die Ergebnisse Statistiken waren sehr ähnlich. Kurz, der Test des überarbeiteten Modells validierte die ursprünglichen HGLM-Modelle, weil die überarbeiteten Modelle auf verschiedenen Proben basierten. Entschuldigen der KONTRAST - und SCHÄTZUNGSAUSRAGUNG Viele Analytiker sind mystifiziert, wie man KONTRAST - und SCHÄTZUNGSanweisungen in SAS benutzt, um eine Vielzahl der allgemeinen linearen Hypothesen (GLH) zu prüfen. GLHs können verwendet werden, um sparsam Schlüsselvergleiche und komplexe Hypothesen zu testen. Allerdings neigt die Einrichtung einer einfachen GLH dazu, einige SAS-Benutzer einzuschüchtern. Beispiele aus verschiedenen Quellen scheinen magisch kommen mit der richtigen Antwort. Der Schlüssel ist zu verstehen, wie die Prozedur das Modell parametrisiert und dann diese Parametrisierung verwendet, um die GLH zu konstruieren. CONTRAST - und and ESTIMATE-Statements finden sich in vielen Modellierungsverfahren im SAS. Allerdings verwenden nicht alle Prozeduren dieselbe Syntax für diese Anweisungen. Diese Präsentation wird die Verwendung der CONTRAST - und ESTIMATE-Anweisungen unter Verwendung von Beispielen in den PROCs GLM, LOGISTIC, MIXED, GLIMMIX und GENMOD entmystifizieren. Kurze Einführung in die Zuverlässigkeitstechnik und PROC ZUVERLÄSSIGKEIT für Nicht-Ingenieure Die Zuverlässigkeitstechnik spezialisiert sich darauf, wie oft ein Produkt oder System unter festgelegten Bedingungen über die Zeit versagt. In der modernen Welt ist es wichtig für ein Produkt oder System hält für eine lange Zeit. Da die Technologie in diesen Tagen gut entwickelt ist, werden einige Systeme schließlich scheitern. Mathematische und statistische Methoden sind für die Quantifizierung und Analyse von Zuverlässigkeitsdaten nützlich. Allerdings ist die wichtigste Priorität der Zuverlässigkeits-Engineering ist es, Engineering-Wissen anwenden, um die Wahrscheinlichkeit von Ausfällen zu verhindern. Dieses Papier stellt die Idee der Zuverlässigkeitstechnik für Nicht-Ingenieure sowie PROC RELIABILITY vor, die einige Anwendungen von Zuverlässigkeitsdaten demonstriert. Simulieren von Warteschlangenmodellen in SAS In diesem Papier werden Benutzer erläutert, wie die Warteschlangenmodelle mit einem Satz von SAS-Makros zu simulieren sind: MM1, MG1 und MMC. Die SAS-Makros simulieren Warteschlangensysteme, in denen Entitäten (wie Kunden, Patienten, Autos oder E-Mail-Nachrichten) ankommen, entweder an einer einzelnen Station oder an mehreren Stationen nacheinander bedient werden, möglicherweise in einer oder mehreren Warteschlangen warten müssen Dann kann gehen. Nach der Simulation liefert SAS eine grafische Ausgabe sowie eine statistische Analyse des gewünschten Warteschlangenmodells. Selektion Bias: Wie kann die Nützlichkeit der Zufallsauswertung Hilfe Kontrolle für sie Eine wichtige Stärke der Beobachtungsstudien ist die Fähigkeit, ein Schlüsselverhalten oder eine Behandlungsmethode auf ein bestimmtes gesundheitliches Ergebnis zu schätzen. Dies ist eine entscheidende Stärke, da die meisten gesundheitlichen Ergebnisse Forschungsstudien nicht in der Lage sind, experimentelle Designs aufgrund ethischer und anderer Einschränkungen zu verwenden. Vor diesem Hintergrund, ein Nachteil der Beobachtungsstudien (die experimentelle Studien natürlich Kontrolle für) ist, dass sie die Fähigkeit, ihre Teilnehmer in Behandlungsgruppen zu randomieren fehlt. Dies kann zu einem unerwünschten Einschluss einer Selektionsvorspannung führen. Eine Möglichkeit, eine Selektionsvorspannung anzupassen, ist die Verwendung einer Neigungstestanalyse. In diesem Papier untersuchen wir ein Beispiel, wie man diese Arten von Analysen zu nutzen. Um zu zeigen, diese Technik, werden wir versuchen, zu untersuchen, ob die jüngsten Substanzmissbrauch hat eine Wirkung auf eine adolescentrsquos Identifizierung von Selbstmordgedanken. Um diese Analyse durchzuführen, wurde eine Selektionsvorspannung identifiziert und die Anpassung durch drei gemeinsame Formen der Neigung bewertet: Schichtung, Anpassung und Regressionsanpassung. Jedes Formular wird gesondert durchgeführt, überprüft und beurteilt, wie seine Wirksamkeit bei der Verbesserung des Modells. Die Daten für diese Studie wurde durch das Youth Risk Behavior Surveillance System, ein laufendes bundesweites Projekt der Centers for Disease Control and Prevention gesammelt. Diese Präsentation ist für jede Ebene von Statistiker, SASreg Programmierer oder Datenanalytiker mit einem Interesse an der Kontrolle für Auswahl Bias konzipiert. Mit SAS zu analysieren Countywide Survey Daten: Ein Blick auf Adverse Childhood Experiences und ihre Auswirkungen auf die langfristige Gesundheit Die negativen Kindheitserfahrungen (ACEs) Skala Maßnahmen Kindheit Exposition gegenüber Missbrauch und Haushalts-Dysfunktion. Forschung schlägt vor, dass ACE mit höheren Risiken des Engagements in riskanten Verhaltensweisen, schlechter Lebensqualität, Morbidität und Mortalität im späteren Leben verbunden sind. In Santa Clara County, einem großen vielfältigen Landkreis, in dem 88 Einwohner Haushalt Internet-Zugang haben, führten wir eine county-wide Behavioral Risk Factor Survey von Erwachsenen mit einer einzigartigen web-basierten Follow-up. Wir führten eine random-digit-dial Telefon-Umfrage (N4.186) und Follow-up-Online-Umfrage mit dem CDC BRFSS ACE-Modul. Von denjenigen, die für die Web-basierte Umfrage, die Antwortrate betrug 33. Die Online-ACE-Modul umfasste 11 Fragen zu 8 Kategorien auf Missbrauch und Haushalts-Dysfunktion zu bilden. PROC SURVEYFREQ und SURVEYLOGISTIC wurden in SAS 9.4 verwendet, um Umfragedaten zu analysieren und grafische Schätzungen für Santa Clara County als Ganzes zur Verfügung zu stellen. Die meisten Befragten (74) berichteten, dass sie 1 ACE erlebt haben. Emotionaler Missbrauch war die häufigste (44), gefolgt von Haushalts-Missbrauch (28), und Haushalts-Geisteskrankheit (25). Die Prävalenz der emotionalen Missbrauch, Haushalt Substanz Missbrauch, körperlicher Missbrauch und Haushalt psychische Erkrankung war am höchsten bei Personen mit hohen (3) und niedrigen (1-2) ACEs. Indikatoren der wahrgenommenen schlechten Gesundheit zeigten eine starke Assoziation zwischen Individuen mit ACEs. Die Chancen auf 1 schlechte psychische Gesundheit Tage im vergangenen Monat waren höher bei Personen mit niedrigen ACEs (OR2.86), hohe ACEs (OR6.74), und bei den Frauen (OR2.27). Eine Web-basierte Umfrage bietet ein zuverlässiges Mittel, um eine Bevölkerung über sensible Themen wie ACE zu niedrigeren Kosten als eine Telefon-Umfrage in kleineren Jurisdiktionen zu bewerten. Die Ergebnisse deuten darauf hin, dass ACE bei Erwachsenen in der Grafschaft häufig sind und in Telefoninterviews möglicherweise unterreagiert werden. PROC SURVEYFREQ und SURVEYLOGISTIC in SAS sind leistungsfähige Werkzeuge, die zur Analyse von Umfragedaten verwendet werden können, insbesondere für Kleinflächenschätzungen über die Gesundheit der Kreise. Wie D-I-D Sie tun, dass grundlegende Unterschiede-in-Unterschiede Modelle in SAS Eine lange Stütze in der Ökonometrie Forschung, Differenz-in-Differenzen (DID) Modelle sind erst vor kurzem häufiger in der Gesundheitsversorgung und epidemiologische Forschung eingesetzt. DID-Studiendesigns sind quasi-experimentell, können mit retrospektiven Beobachtungsdaten verwendet werden und erfordern keine Expositions-Randomisierung. Dieses Studiendesign schätzt die Differenz der Vor-Posten-Veränderungen in einem Ergebnis, das eine exponierte Gruppe mit einer nicht-exponierten (Referenz-) Gruppe vergleicht. Die Ergebnisänderung in der nicht exponierten Gruppe schätzt die erwartete Veränderung in der exponierten Gruppe, wenn die Gruppe kontrapunktisch nicht exponiert war. Durch Subtrahieren dieser Änderung von der Veränderung der exponierten Gruppe (die ldquodifferenz in den Differenzen) werden die Effekte der säkularen Hintergrundtrends entfernt. In dem grundlegenden DID-Modell dient jedes Subjekt als seine eigene Kontrolle, wobei das Verwechseln durch bekannte und unbekannte individuelle Faktoren, die mit dem Ergebnis von Interesse assoziiert sind, entfernt wird. Somit erzeugt die DID eine kausale Schätzung der Änderung in einem Ergebnis, das mit der Initiierung der Belichtung von Interesse assoziiert ist, während sie für Vorspannungen aufgrund säkularer Trends und Verwechslungen steuert. Ein einfaches generalisiertes allgemeines lineares Modell liefert Schätzungen von Bevölkerungs-Durchschnitts-Pisten zwischen zwei Zeitpunkten für die exponierten und nicht-exponierten Gruppen und testet, ob sich die Pisten unterscheiden, indem sie einen Interaktions-Term zwischen der Zeit - und der Expositionsvariablen einschließen. In dieser Arbeit veranschaulichen wir die Konzepte hinter dem grundlegenden DID-Modell und präsentieren SAS-Code für den Betrieb dieser Modelle. We include a brief discussion of more advanced DID methods and present an example of a real-world analysis using data from a study on the impact of introducing a value-based insurance design (VBID) medication plan at Kaiser Permanente Northern California on change in medication adherence. Using PROC PHREG to Assess Hazard Ratio in Longitudinal Environmental Health Study Air pollution, especially combustion products, can activate metabolic disorders through inflammatory pathways potentially leading to obesity. The effect of air-pollution on BMI growth was shown by a previous study (Jerrett, et al. 2014). Recognizing the role of air pollution in the development of obesity in children can help guide possible interventions reducing obesity formation. The objective of this paper is to analyze the obesity incidence of children participating in Childrenrsquos Hospital Study (CHS) who were non-obese at baseline, identify the time interval for the onset of obesity, and identify the effects of various risk factors, especially air pollutants. The PROC PHREG procedure was used in creating a model within a macro that included community random effects, stratified by sex, and adjusting for baseline characteristics. Using PROC LOGISTIC for Conditional Logistic Regression to Evaluate Vehicle Safety Performance The LOGISTIC Procedure has several capabilities beyond standard logistic regression on binary outcome variables. For a conditional logit model, PROC LOGISTIC can perform several types of matching, 1:1, 1:M matching, and even M:N matching. This paper shows an example of using PROC LOGISTIC for conditional logit models to evaluate vehicle safety performance in fatal accidents using the Fatality Analysis Reporting System (FARS) 2004-2011 database. Conditional logistic regression models were performed with an additional stratum parameter to model the relationship between fatality of the drivers and the vehiclersquos continent of origin. Identifying Duplicates Made Easy Elizabeth Angel and Yunin Ludena Have you ever had trouble removing or finding the exact type of duplicate you want SAS offers several different ways to identify, extract, andor remove duplicates, depending on exactly what you want. We will start by demonstrating perhaps the most commonly used method, PROC SORT, and the types of duplicates it can identify and how to remove, flag, or store them. Then, we will present the other less commonly used methods which might give information that PROC SORT cannot offer, including the data step (FIRST. LAST.), PROC SQL, PROC FREQ, and PROC SUMMARY. The programming is demonstrated at a beginnerrsquos level. Dont Forget About Small Data Beginning in the world of data analytics and eventually flowing into mainstream media, we are seeing a lot about Big Data and how it can influence our work and our lives. Through examples, this paper will explore how Small Data - ndash which is everything Big Data is not - ndash can and should influence our programming efforts. The ease with which we can read and manipulate data from different formats into usable tables in SASreg makes using data to manage data very simple and supports healthy and efficient practices. This paper will explore how using small or summarized data can help to organize and track program development, simplify coding and optimize code. Let the CAT Out of the Bag: String Concatenation in SASreg 9 Are you still using TRIM, LEFT, and vertical bar operators to concatenate strings Its time to modernize and streamline that clumsy code by using the string concatenation functions introduced in SASreg 9. This paper is an overview of the CAT, CATS, CATT, and CATX functions introduced in SASreg 9, and the new CATQ function added in SASreg 9.2. In addition to making your code more compact and readable, this family of functions also offers some new tricks for accomplishing previously cumbersome tasks. SASreg Abbreviations: a Shortcut for Remembering Complicated Syntax SASreg Abbreviations: a Shortcut for Remembering Complicated Syntax Yaorui Liu, Department of Preventive Medicine, University of Southern California ABSTRACT One of many difficulties for a SASreg programmer is remembering how to accurately use SAS syntax, especially the ones that include many parameters. Not mastering the basic syntax parameters by heart will definitely make onersquos coding inefficient because one would have to check the SAS reference manual constantly to ensure that onersquos syntax was implemented properly. One of the more useful tools in SAS, but seldom known by novice programmers, is the use of SAS Abbreviations. It allows users to store text strings, such as the syntax of a DATA step function, a SAS procedure, or a complete DATA step, with a user-defined and easy-to-remember abbreviated term. Once this abbreviated term is typed within the enhanced editor, SAS will automatically bring-up the corresponding stored syntax. Knowing how to use SAS Abbreviations will ultimately be beneficial to programmers with varying levels of SAS expertise. In this paper, various examples by utilizing SAS Abbreviations will be demonstrated. Implementation of Good Programming Practices in Clinical SAS SASreg Base software provides users with many choices for accessing, manipulating, analyzing, and processing data and results. Partly due to the power offered by the SAS software and the size of data sources, many application developers and end-users are in need of guidelines for more efficient use. This presentation highlights my personal top ten list of performance tuning techniques for SAS users to apply in their applications. Attendees learn DATA and PROC step language statements and options that can help conserve CPU, IO, data storage, and memory resources while accomplishing tasks involving processing, sorting, grouping, joining (merging), and summarizing data. Sorting a Bajillion Records: Conquering Scalability in a Big Data World quotBig dataquot is often distinguished as encompassing high volume, velocity, or variability of data. While big data can signal big business intelligence and big business value, it also can wreak havoc on systems and software ill-prepared for its profundity. Scalability describes the ability of a system or software to adequately meet the needs of additional users or its ability to utilize additional processors or resources to fulfill those added requirements. Scalability also describes the adequate and efficient response of a system to increased data throughput. Because sorting data is one of the most common as well as resource-intensive operations in any software language, inefficiencies or failures caused by big data often are first observed during sorting routines. Much SASreg literature has been dedicated to optimizing big data sorts for efficiency, including minimizing execution time and, to a lesser extent, minimizing resource usage (i. e. memory and storage consumption.) Less attention has been paid, however, to implementing big data sorting that is reliable and robust even when confronted with resource limitations. To that end, this text introduces the SAFESORT macro that facilitates a priori exception handling routines (which detect environmental and data set attributes that could cause process failure) and post hoc exception handling routines (which detect actual failed sorting routines.) If exception handling is triggered, SAFESORT automatically reroutes program flow from the default sort routine to a less resource-intensive routine, thus sacrificing execution speed for reliability. However, because SAFESORT does not exhaust system resources like default SAS sorting routines, in some cases it performs more than 200 times faster than default SAS sorting methods. Macro modularity moreover allows developers to select their favorite sorting routine and, for data-driven disciples, to build fuzzy logic routines that dynamically select a sort algorithm based on environmental and data set attributes. SAS integration with NoSQL database We are living in the world of abundant data, so called ldquobig datardquo. The term ldquobig datardquo is closely associated with any structured data ndash unstructured, structured and semi-structured. They are called ldquounstructuredrdquo and ldquosemi-structuredrdquo because they do not fit neatly in a traditional row-column relational database. A NoSQL (Not only SQL or Non-relational SQL) database is the type of database that can handle any structured data. For example, a NoSQL database can store any structured data such as XML (Extensible Markup Language), JSON (JavaScript Object Notation) or RDF (Resource Description Framework) files. If an enterprise is able to extract any structured data from NoSQL databases and transfer it to the SAS environment for analysis, it will produce tremendous value, especially from a big data solutions standpoint. This paper will show how any structured data is stored in the NoSQL databases and ways to transfer it to the SAS environment for analysis. First, the paper will introduce the NoSQL database. For example, NoSQL databases can store any structured data such as XML, JSON or RDF files. Secondly, the paper will show how the SAS system connects to NoSQL databases using REST (Representational State Transfer) API (Application Programming Interface). For example, SAS programmers can use the PROC HTTP option to extract XML or JSON files through REST API from the NoSQL database. Finally, the paper will show how SAS programmers can convert XML and JSON files to SAS datasets for analysis. For example, SAS programmers can create XMLMap files using XMLV2 LIBNAME engine and convert the extracted XML files to SAS datasets. DS2 Versus Data Step: Efficiency Considerations There is recognition that in large, complex systems the advantages of object-oriented concepts available in DS2 of modularity, code reuse and ease of debugging can provide increased efficiency. Object-oriented programming also allows multiple teams of developers to work on the same project easily. DS2 was designed for data manipulation and data modeling applications that can achieve increased efficiency by running code in threads, splitting the data across multiple processors and disks. Of course, performance is also dependent on hardware architecture and the amount of effort you put into the tuning of your architecture and code. Join our panel for a discussion of architecture, tuning and data size considerations in determining if DS2 is the more efficient alternative. Using Shared Accounts in Kerberized Hadoop Clusters with SASreg: How Can I Do That Using shared accounts to access third-party data servers is a common architecture in SASreg environments. SAS software can support seamless user access to shared accounts in databases such as Oracle, via group definitions and outbound authentication domains in Metadata. However, the configurations necessary to leverage shared accounts in Hadoop clusters with Kerberos authentication are more complicated. Not only must Kerberos tickets be generated and maintained in order to simply access the Hadoop environment, but those tickets must allow access as the shared account instead of the individual usersrsquo accounts. Methods for implementing this arrangement in SAS environments can be non-intuitive. This paper starts by outlining several general architectures of shared accounts in Kerberized Hadoop environments. It then presents possible methods of managing such shared account access in SAS environments, including specific implementation details, code samples and security implications. Finally, troubleshooting methods are presented for when issues arise. Example code and configurations for this paper were developed on a SAS 9.4 system running over Redhat Enterprise Linux 6. What just happened A visual tool for highlighting differences between two data sets. Base SAS includes a great utility for comparing two data sets - PROC COMPARE. The output though can be hard to read as the differences between values are listed separately for each variable. Its hard to see the differences across all variables for the same observation. This talk presents a macro to compare two SAS data sets and display the differences in Excel. PROC COMPARE OUT option creates an output data set with all the differences. This data set is then processed with PROC REPORT using ODS EXCEL and colour highlighting to show the differences in an Excel, making the differences easy to see. Tips and Tricks for Producing Time-Series Cohort Data Developers working on a production process need to think carefully about ways to avoid future changes that require change control, so its always important to make the code dynamic rather than hardcoding items into the code. Even if you are a seasoned programmer, the hardcoded items might not always be apparent. This paper assists in identifying the harder-to-reach hardcoded items and addresses ways to effectively use control tables within the SASreg software tools to deal with sticky areas of coding such as formats, parameters, groupinghierarchies, and standardization. The paper presents examples of several ways to use the control tables and demonstrates why this usage prevents the need for coding changes. Practical applications are used to illustrate these examples. The Power of the Function Compiler: PROC FCMP PROC FCMP, the user-defined function procedure, allows SAS users of all levels to get creative with SAS and expand their scope of functionality. PROC FCMP is the superhero of all SAS functions in its vast capabilities to create and store uniquely defined functions that can later be used in data steps. This paper outlines the basics as well as tips and tricks for the user to get the most out of this procedure. Creating Viable SASreg Data Sets From Survey Monkeyreg Transport Files Survey Monkey is an application that provides a means for creating online surveys. Unfortunately, the transport (Excel) file from this application requires a complete overhaul in order to do any serious data analysis. Besides having a peculiar structure and containing extraneous data points, the column headers become very problematic when importing the file into SAS. In fact, the initial SAS data set is virtually unusable. This paper explains a systematic approach for creating a viable SAS data set for doing serious analysis. Document and Enhance Your SASreg Code, Data Sets, and Catalogs with SAS Functions, Macros, and SAS Metadata Roberta Glass and Louise Hadden Discover how to document your SASreg programs, data sets, and catalogs with a few lines of code that include SAS functions, macro code, and SAS metadata. Do you start every project with the best of intentions to document all of your work, and then fall short of that aspiration when deadlines loom Learn how your programs can automatically update your processing log. If you have ever wondered who ran a program that overwrote your data, SAS has the answer And If you donrsquot want to be tracing back through a yearrsquos worth of code to produce a codebook for your client at the end of a contract, SAS has the answer Donrsquot Get Blindsided by PROC COMPARE For a statistical programmer in the pharmaceutical industry each work day is new. A project you have been working on for a few months can be changed at a momentrsquos notice and you need to implement changes quickly and accurately. To ensure that the desired changes are done quickly, and most especially accurately, if the task entails doing a find and replace sort of thing in all the SAS Programs in a directory (or multiple directories) a macro called ldquoReplacerrdquo could come to the rescue. Process Flow: First, it reads all the SAS programs in a directory one by one and converts every SAS program to a SAS dataset using grepline. After this, it reads all datasets, one by one. replacing an existing string with the now desired string using if then conditional logic. Finally, it outputs each updated SAS dataset as a new SAS program at a desired location which has been specified. This macro has multiple parameters which you can specify: the input directory the output directory and the from and to strings which gives the programmer more control over the process. A quick example of the practical use of the replacer macro is ndash when making the transition from a Windows to UNIX Server we needed to make sure we changed the path of our init. sas and changed all forward slashes() to backward slashes ().Letrsquos assume we have 100 programs and we decide to do this manually. It can be a cumbersome task and given time constraints, accuracy is not guaranteed. The programmer may end up spending a couple of hours to complete the necessary changes to each program before re-running all the programs to make sure the appropriate changes have taken place. Replacer can accomplish this same task in less than 2 minutes. Ditch the Data Memo: Using Macro Variables and Outer Union Corresponding in PROC SQL to Create Data Set Summary Tables Data set documentation is essential to good programming and for sharing data set information with colleagues who are not SAS programmers. However, most SAS programmers dislike writing memos which must be updated each time a dataset is manipulated. Utilizing two tools, macro variables and the outer union corresponding set operator in PROC SQL, we can write concise code that exports a single summary table containing important data set information serving in lieu of data memos. These summary tables can contain the following data set information and much more: 1) Report in the change in the number of records in a dataset due to dropping records, collapsing across IDs, removing duplicate records 2) summary statistics of key variables and 3) trends across time. This presentation requires some basic understanding of macros and SQL queries. File Management Using Pipes and X Commands in SASreg SAS for Windows can be an extremely powerful piece of software, not only for analyzing data, but also for organizing and maintaining output and permanent datasets. By employing pipes and operating system (lsquoXrsquo) commands within a SAS session, you can easily and effectively manage files of all types stored on your local network. Handling longitudinal data from multiple sources: experience with analyzing kidney disease patients Elani Streja and Melissa Soohoo Analyses in health studies using multiple data sources often come with a myriad of complex issues such as missing data, merging multiple data sources and date matching. Combining multiple data sources is not straight forward, as often times there is discordance or missing information such as dates of birth, dates of death, and even demographic information such as sex, race, ethnicity and pre-existing comorbidities. It therefore becomes essential to document the data source from which the variable information was retrieved. Analysts often rely on one resource as the dominant variable to use in analyses and ignore information from other sources. Sometimes, even the database thought to be the ldquogold standardrdquo is in fact discordant with other data sources. In order to increase sensitivity and information capture, we have created a source variable, which demonstrates the combination of sources for which the data was concordant and derived. In our example, we will show how to resolve address information on date of birth, date of death, date of transplant, sex and race combined from 3 data sources with information on kidney disease patients. These 3 sources include: the United States Renal Data System, Scientific Registry of Transplant Recipients, and data from a large dialysis organization. This paper focuses on approaches of handling multiple large databases for preparation for analyses. In addition, we will show how to summarize and prepare longitudinal lab measurements (from multiple sources) for use in analyses. An Array of Fun: Macro Variable Arrays Like all skilled tradespeople, SASreg programmers have many tools at their disposal. Part of their expertise lies in knowing when to use each tool. In this paper, we use a simple example to compare several common approaches to generating the requested report: the TABULATE, TRANSPOSE, REPORT, and SQL procedures. We investigate the advantages and disadvantages of each method and consider when applying it might make sense. A variety of factors are examined, including the simplicity, reusability, and extensibility of the code in addition to the opportunities that each method provides for customizing and styling the output. The intended audience is beginning to intermediate SAS programmers. Something Old, Something New. Flexible Reporting with DATA Step-based Tools The report looks simple enoughmdasha bar chart and a table, like something created with the GCHART and REPORT procedures. But, there are some twists to the reporting requirements that make those procedures not quite flexible enough. The solution was to mix quotoldquot and quotnewquot DATA step-based techniques to solve the problem. Annotate datasets are used to create the bar chart and the Report Writing Interface (RWI) to create the table. Without a whole lot of additional code, an extreme amount of flexibility is gained. The goal of this paper is to take a specific example of a couple generic principles of programming (at least in SASreg): 1. The tools you choose are not always the most obvious ones ndash So often, other from habit of comfort level, we get zeroed in on specific tools for reporting tasks. Have you ever heard anyone say, ldquoI use TABULATE for everythingrdquo or ldquoIsnrsquot PROC REPORT wonderful, it can do anythingrdquo While these tools are great (Irsquove written papers on their use), itrsquos very easy to get into a rut, squeezing out results that might have been done more easily, flexibly or effectively with something else. 2. Itrsquos often easier to make your data fit your reporting than to make your reporting fit your data ndash It always takes data to create a report and itrsquos very common to let the data drive the report development. We struggle and fight to get the reporting procedures to work with our data. There are numerous examples of complicated REPORT or TABULATE code that works around the structure of the data. However, the data manipulation tools in SAS (data step, SQL, procedure output) can often be used to preprocess the data to make the report code significantly simpler and easier to maintain and modify. Proc Document, The Powerful Utility for ODS Output The DOCUMENT procedure is a little-known procedure that can save you vast amounts of time and effort when managing the output of your SASreg programming efforts. This procedure is deeply associated with the mechanism by which SAS controls output in the Output Delivery System (ODS). Have you ever wished you didnrsquot have to modify and rerun the report-generating program every time there was some tweak in the desired report PROC DOCUMENT enables you to store one version of the report as an ODS Document Object and then call it out in many different output forms, such as PDF, HTML, listing, RTF, and so on, without rerunning the code. Have you ever wished you could extract those pages of the output that apply to certain ldquoBY variablesrdquo such as State, StudentName, or CarModel With PROC DOCUMENT, you have where capabilities to extract these. Do you want to customize the table of contents that assorted SAS procedures produce when you make frames for the table of contents with HTML, or use the facilities available for PDF PROC DOCUMENT enables you to get to the inner workings of ODS and manipulate them. This paper addresses PROC DOCUMENT from the viewpoint of end results, rather than provide a complete technical review of how to do the task at hand. The emphasis is on the benefits of using the procedure, not on detailed mechanics. There will be a number of practical applications presented for everyday real life challenges that arise in manipulating output in HTML, PDF and RTF formats. A SAS macro for quick descriptive statistics Arguably, the most required table in publications is the description of the sample table, fondly referred to among statisticians as ldquoTable 1rdquo. This table displays means and standard errors, medians and IQRs, and counts and percentages for the variables in the sample, often stratified by some variable of interest (e. g. disease status, recruitment site, sex, etc.). While this table is extremely useful, the construction of it can be time consuming and, frankly, rather boring. I will present two SAS macros that facilitate the creation of Table 1. The first is a ldquoquick and dirtyrdquo macro that will output the results for Table 1 for nearly every situation. The second is a ldquoprettyrdquo macro that will output a well formatted Table 1 for a specific situation. Controlling Colors by Name Selecting, Ordering, and Using Colors for Your Viewing Pleasure Within SASreg literally millions of colors are available for use in our charts, graphs, and reports. We can name these colors using techniques which include color wheels, RGB (Red, Green, Blue) HEX codes, and HLS (Hue, Lightness, Saturation) HEX codes. But sometimes I just want to use a color by name. When I want purple, I want to be able to ask for purple not CX703070 or H03C5066. But am I limiting myself to just one purple What about light purple or pinkish purple. Do those colors have names or must I use the codes It turns out that they do have names. Names that we can use. Names that we can select, names that we can order, names that we can use to build our graphs and reports. This paper will show you how to gather color names and manipulate them so that you can take advantage of your favorite purple be it lsquopurplersquo, lsquograyish purplersquo, lsquovivid purplersquo, or lsquopale purplish bluersquo. Much of the control will be obtained through the use of user defined formats. Learn how to build these formats based on a data set containing a list of these colors. Tweaking your tables: Suppressing superfluous subtotals in PROC TABULATE PROC TABULATE is a great tool for generating cross tab style reports. Its very flexible but has a few annoying limitations. One is suppressing superfluous subtotals. The ALL keyword creates a total or subtotal for the categories in one dimension. However if there is only one category in the dimension, the subtotal is still shown, which is really just repeating the detail line again. This can look a bit strange. This talk demonstrates a method to suppress those superfluous totals by saving the output from PROC TABULATE using the OUT option. That data set is then reprocessed to remove the undesirable totals using the TYPE variable which identifies the total rows. PROC TABULATE is then run again against the reprocessed data set to create the final table. Indenting with Style Within the pharmaceutical industry, may SAS programmers rely heavily on Proc Report. While it is used extensively for summary tables and listings, it is more typical that all processing is done prior to final report procedure rather than using some of its internal functionality. In many of the typical summary tables, some indenting is required. This may be required to combine information into a single column in order to gain more printable space (as is the case with many treatment group columns). It may also be to simply make the output more aesthetically pleasing. A standard approach it to pad a character string with spaces to give the appearance of indenting. This requires pre-processing of the data as well as the use of the ASISON option in the column style. While this may be sufficient in many cases, it fails for longer text strings that require wrapping within a cell. Alternative approaches that conditionally utilize INDENT and LEFTMARGIN options of a column style are presented. This Quick-tip presentation will describe such options for indenting. Example outputs will be provided to demonstrate the pros and cons of each. The use of Proc Report and ODS is required in this application using SAS 9.4 in a Windows environment. SASreg Office Analytics: An Application In Practice Data Monitoring and Reporting Using Stored Process Mansi Singh, Kamal Chugh, Chaitanya Chowdagam and Smitha Krishnamurthy Time becomes a big factor when it comes to ad-hoc reporting and real-time monitoring of data while the project work is on full swing. There are always numerous urgent requests from various cross-functional groups regarding the study progress. Typically a programmer has to work on these requests along with the study work which can become stressful. To address this growing need of real-time monitoring of data and to tailor the requirements to create portable reports, SASreg has introduced a powerful tool called SAS Office Analytics. SAS Office Analytics with Microsoftreg Add-In provides excellent real-time data monitoring and report generating capabilities with which a SAS programmer can take ad-hoc requests and data monitoring to next level. Using this powerful tool, a programmer can build interactive customized reports as well as give access to study data, and anyone with knowledge of Microsoft Office can then view, customize, andor comment on these reports within Microsoft Office with the power of SAS running in the background. This paper will be a step-by-step guide to demonstrate how to create these customized reports in SAS and access study data using Microsoft Office Add-In feature. Getting it done with PROC TABULATE From state-of-the-art research to routine analytics, the Jupyter Notebook offers an unprecedented reporting medium. Historically tables, graphics, and other output had to be created separately and integrated into a report piece by piece amidst the drafting of the text. The Jupyter Notebook interface allows for the creation of code cells and markdown cells in any kind of arrangement. While the markdown cells admit all the typical sorts of formatting, the code cells can be used to run code within and throughout the document. In this way, report creation happens naturally and in a completely reproducible way. Handing a colleague a Jupyter Notebook file to be re-run or revised is much easier and simpler than passing along at least two files: the code and the text. With the new SAS reg kernel for Jupyter, all of this is possible and more Clinton vs. Trump 2016: Analyzing and Visualizing Sentiments towards Hillary Clinton and Donald Trumprsquos Policies Sid Grover and Jacky Arora The United States 2016 presidential election has seen an unprecedented media coverage, numerous presidential candidates and acrimonious debate over wide-ranging topics from candidates of both the republican and the democratic party. Twitter is a dominant social medium for people to understand, express, relate and support the policies proposed by their favorite political leaders. In this paper, we aim to analyze the overall sentiment of the public towards some of the policies proposed by Donald Trump and Hillary Clinton using Twitter feeds. We have started to extract the live streaming data from Twitter. So far, we have extracted about 200,000 twitter feeds accessing the live stream API of Twitter, using a java program mytwitterscraper which is an open source real-time twitter scraper. We will use SASreg Enterprise Miner and SASreg Sentiment Analysis Studio to describe and assess how people are reacting to each candidatersquos stand on issues such as immigration, taxes and so on. We will also track and identify patterns of sentiments shifting across time (from March to June) and geographic regions. Donor Sentiment Analysis of Presidential Primary Candidates Using SAS In this paper, we explore advantages of using PROC DS2 procedure over the data step programming in SASreg. DS2 is a new SAS proprietary programming language that is appropriate for advanced data manipulation. We explore use of PROC DS2 to execute queries in databases using FED SQL from within the DS2 program. Several DS2 language elements accept embedded FedSQL syntax, and the run-time generated queries can exchange data interactively between DS2 and supported database. This action enables SQL preprocessing of input tables, which effectively allows processing data from multiple tables in different databases within the same query thereby drastically reducing processing times and improving performance. We explore use of DS2 for creating tables, bulk loading tables, manipulating tables, and querying data in an efficient manner. We explore advantages of using PROC DS2 over data step programming such as support for different data types, ANSI SQL types, programming structure elements, and benefits of using new expressions or writing onersquos own methods or packages available in the DS2 system. We also explore high-performance version of the DS2 procedure, PROC HPDS2, and show how one can submit DS2 language statements for execution to either a single machine running multiple threads or to a distributed computing environment, including the SAS LASR Analytic Server thereby massively reducing processing times resulting in performance improvement. The DS2 procedure enables users to submit DS2 language statements from a Base SAS session. The procedure enables requests to be processed by the DS2 data access technology that supports a scalable, threaded, high-performance, and standards-based way to access, manage, and share relational data. In the end, we empirically measure performance benefits of using PROC DS2 over PROC SQL for processing queries in-database by taking advantage of threaded processing in supported data databases such as Oracle. Social Media, Anonymity, and Fraud: HP Forest Node in SASreg Enterprise Minertrade You may encounter people who used SASreg long ago (perhaps in university) or through very limited use in a job. Some of these people with limited knowledgeexperience think that the SAS system is ldquojust a statistics packagerdquo or ldquojust a GUIrdquo, the latter usually a reference to SASreg Enterprise Guidereg or if a dated reference, to (legacy) SASAFreg or SASFSPreg applications. The reality is that the modern SAS system is a very large, complex ecosystem, with hundreds of software products and a diversity of tools for programmers and users. This poster provides a set of diagrams and tables that illustrate the complexity of the SAS system, from the perspective of a programmer. Diagramsillustrations that are provided here include: the different environments that program code can run in cross-environment interactions and related tools SAS Grid: parallel processing SAS can run with files in memory ndash the legacy SAFILE statement and big dataHadoop some code can run in-database. We end with a tabulation of the many programming languages and SQL dialects that are directly or indirectly supported within SAS. Hopefully the content of this poster will inform those who think that SAS is an old, dated statistics package or just a simple GUI. Leadership: More than Just a Position Laws of Programming Leadership As someone studying statistics in the data science era, more and more emphasis is put on illustrious graphs. Data is no longer displayed with a black and white boxplot. Using SASreg MACRO and the Statistical Graphics procedure, you can animate graphs to turn an outdated two variable graph into a graph in motion that shows not only a relation between factors but also a change over time. An even simpler approach for bubble graphs is to use a function in JMP to create colorful moving plots that would typically require many lines of code, with just a few clicks of the mouse. Sentiment Analysis of Opinions about Self-driving cars Swapneel Deshpande and Nachiket Kawitkar Self-driving cars are no longer a futuristic dream. In recent past, Google launched a prototype of the self-driving car while Apple is also developing its own self-driving car. Companies like Tesla have just introduced an Auto Pilot version in their newer version of electric cars which have created quite a buzz in the car market. This technology is said to enable aging or disable people to drive around without being dependent on anyone while also might affecting the accident rate due to human error. But many people are still skeptical about the idea of self-driving cars and thatrsquos our area of interest. In this project, we plan to do sentiment analysis on thoughts voiced by people on the Internet about self-driving cars. We have obtained the data from crowdflowerdata-for-everyone which contain these reviews about the self-driving cars. Our dataset contains 7,156 observations and 9 variables. We plan to do descriptive analysis of the reviews to identify key topics and then use supervised sentiment analysis. We also plan to track and report at how the topics and the sentiments change over time. An Analysis of the Repetitiveness of Lyrics in Predicting a Songrsquos Popularity In the interest of understanding whether or not there is a correlation between the repetitiveness of a songrsquos lyrics and its popularity, the top ten songs from the year-end Billboard Hot 100 Songs chart from 2002 to 2015 were collect. These songs then had their lyrics assessed to determine the count of the top ten words used. These words counts were then used to predict the number of weeks the song was on the chart. The prediction model was analyzed to determine the quality of the model and if word count is a significant predictor of a songrsquos popularity. To investigate if song lyrics are becoming more simplistic over time there were several tests completed in order to see if the average word counts have been changing over the years. All analysis was completed in SASreg using various PROCs. Regression Analysis of the Levels of Chlorine in the Public Water Supply in Orange County, FL This conference provides a range of events that can benefit any and all SAS Users. However, sometimes the extensive schedule can be overwhelming at first glance. With so many things to do and people to see, I have compiled the advice I was given as a novice WUSS and lessons Irsquove learned since. This presentation will provide a catalog of tips to make the most out of anyonersquos conference experience. From volunteering, to the elementary advice of sitting at a table where you do not know anyonersquos name, listeners will be excited to take on all that WUSS offers. Patients with Morbid Obesity and Congestive Heart Failure Have Longer Operative Time and Room Time in Total Hip Arthroplasty More and more patients with total hip arthroplasty have obesity, and previous studies have shown a positive correlation between obesity and increased operative time in total hip arthroplasty. But those studies shared the limitation of small sizes. Decreasing operative time and room time is essential to meeting the increased demand for total hip arthroplasty, and factors that influence these metrics should be quantified to allow for targeted reduction in time and adjusted reimbursement models. This study intend to use a multivariate approach to identify which factors increase operative time and room time in total hip arthroplasty. For the purposes of this study, the American College of Surgeons National Surgical Quality Improvement Program database was used to identify a cohort of over thirty thousand patients having total hip arthroplasty between 2006 and 2012. Patient demographics, comorbidities including body mass index, and anesthesia type were used to create generalized linear models identifying independent predictors of increased operative time and room time. The results showed that morbid obesity (body mass index gt40) independently increased operative time by 13 minutes and room time 18 by minutes. Congestive heart failure led to the greatest increase in overall room time, resulting in a 20-minute increase. Anesthesia method further influenced room time, with general anesthesia resulting in an increased room time of 18 minutes compared with spinal or regional anesthesia. Obesity is the major driver of increased operative time in total hip arthroplasty. Congestive heart failure, general anesthesia, and morbid obesity each lead to substantial increases in overall room time, with congestive heart failure leading to the greatest increase in overall room time. All analyses are conducted via SAS (version SAS 9.4, Cary, NC). Using SAS: Monte Carlo Simulations of Manufactured Goods - Should-Cost Models Should cost modeling, or ldquocleansheetingrdquo, of manufactured goods or services is a valuable tool for any procurement group. It provides category managers a foundation to negotiate, test and drive value addedvalue engineering ideas. However, an entire negotiation can be derailed by a supplier arguing that certain assumptions or inputs are not reflective of what they are currently seeing in their plant. The most straightforward resolution to this issue is using a Monte Carlo simulation of the cleansheet. This enables the manager to prevent any derailing supplier tangents, by providing them with the information in regards to how each input effects the model as a whole, and the resulting costs. In this ePoster, we will demonstrate a method for employing a Monte Carlo simulation on manufactured goods. This simulation will cover all of the direct costs associated with production, labor, machine, material, as well as the indirect costs, i. e. overhead, etc. Using SAS, this simulation model will encompass 60 variables, from nine discrete manufacturing processes, and will be set to automatically output the information most relevant to the category manager. Making Prompts Work for You: Using SAS Enterprise Guide Prompts with Categorization of Output Edward Lan and Kai-Jen Cheng In statistical and epidemiology units of public health departments, SAS codes are often re-used across a variety of different projects for data cleaning and generation of output datasets from the databases. Each SAS user will copy and paste common SAS codes into their own program and use it to generate datasets for analysis. In order to simplify this process, SAS Enterprise Guide (EG) prompts can be used to eliminate the need for the user to edit the SAS code or copy and paste. Instead, the user will be able to enter the desired directory, date ranges, and desired variables to be included in the dataset. In the event of large datasets, however, it is beneficial for these variables to be grouped into categories instead of having the user individually choose the desired variables or lumping all the variables into the final dataset. Using the SAS EG prompt for static lists where the SAS user selects multiple values, variable categories can be created for selection where groups of variables are selected into the dataset. In this paper for novice and intermediate SAS users, we will discuss how macros and SAS EG prompts, using EG 7.1, can be used to automate the process of generating an output dataset where the user selects a folder directory, date ranges, and categories of variables to be included in the final dataset. Additionally, the paper will explain how to overcome issues with integrating the categorization prompt with generating the output dataset. Application of Data Mining Techniques for Determining Factors Associated with Overweight and Obesity Among California Adults This paper describes the application of supervised data mining methods using SAS Enterprise Miner 12.3 on data from the 2013-2014 California Health Interview Survey (CHIS), in order to better understand obesity and the indicators that may predict it. CHIS is the largest health survey ever conducted in any state, which samples California households through random-digit-dialing (RDD). EM was used to apply logistic regression, decision trees and neural network models to predict a binary variable, OverweightObese Status, which determines whether an individual has a Body Mass Index (BMI) greater than 25. These models were compared to assess which categories of information, such as demographic factors or insurance status, and individual factors like race, best predict whether an individual is overweightobese or not. The Orange Lifestyle If you are like many SAS users you have worked with the classical quotoldquot SAS graphics procedures for some time and are very comfortable with the code syntax, workflow approach etc that make for reasonably simple creation of presentation graphics. Then all of a sudden, a job requires the capabilities of the procedures in SAS ODS graphics. At first glance you may be thinking --- quotOK, a few more procedures to learn and a little syntax to learnquot. Then you realize that moving yourself into this arena is no small task. This presentation will overview the options and approaches that you might take to get up to speed fast. Included will be decision trees to be followed in deciding upon a course of action. This paper contains many examples of very simple ways to get very simple things accomplished. Over 20 different graphs are developed using only a few lines of code each, using data from the SASHELP data sets. The usage of the SGPLOT, SGPANEL, and SGSCATTER procedures are shown. In addition, the paper addresses those situations in which the user must alternatively use a combination of the TEMPLATE and SGRENDER procedures to accomplish the task at hand. Most importantly, the use of ODS Graphics Designer as a teaching tool and a generator of sample graphs and code are covered. A single slide in the presentation overviewing the ODS Designer shows everything needed to generated a very complex graph. The emphasis in this paper is the simplicity of the learning process. Users will be able to take the included code and run it immediately on their personal machines to achieve an instant sense of gratification. The paper also addresses the quotODS Sandwichquot for creating output and the use of Proc Document to manipulate it. Exploring Multidimensional Data with Parallel Coordinate Plots Throughout the many phases of an analysis, it may be more intuitive to review data statistics and modeling results as visual graphics rather than numerical tables. This is especially true when an objective of the analysis is to build a sense of the underlying structures within the data rather than describe the data statistics or model results with numerical precision. Although scatterplots provide a means of evaluating relationships, its two-dimensional nature may be limiting when exploring data across multiple dimensions simultaneously. One tool to explore multivariate data is parallel coordinate plots. I will present a method of producing parallel coordinate plots using PROC SGPLOT and will provide examples of when parallel coordinate plots may be very informative. In particular, I will discuss its application on an analysis of longitudinal observational data and results from unsupervised classification techniques. Making SAS the Easy Way Out: Harnessing the Power of PROC TEMPLATE to Create Reproducible, Complex Graphs With high pressure deadlines and mercurial collaborators, creating graphs in the most familiar way seems like the best option. Using post-processing programs like Photoshop or Microsoft Powerpoint to modify graphs is quicker and easier to the novice SAS User or for onersquos collaborators to do on their own. However, reproducibility is a huge issue in the scientific community. Any changes made outside statistical software need to be repeated when collaborator preferences change, the data changes, the journal requires additional elements, and a host of other reasons The likelihood of making errors increases along with the time spent making the figure. Learning PROC TEMPLATE allows one to seamlessly create complex, automatically generated figures and eliminates the need for post-processing. This paper will demonstrate how to do complex graph manipulation procedures in SAS 9.3 or later to solve common problems, including lattice panel plots for different variables, split plots and broken axes, weighted panel plots, using select observations in each panel, waterfall plots, and graph annotation. The examples presented are healthcare based, but the methods are applicable to finance, business and education. Attendees should have a basic understanding of the macro language, graphing in SAS using SGPLOT, and ODS graphics. Customizing plots to your heartrsquos content using PROC GPLOT and the annotate facility This paper introduces tips and techniques that can speed up the validation of 2 datasets. It begins with a brief introduction to PROC COMPARE, then proceeds to introduce some techniques without using automation to that can help to speed up the validation process. These techniques are most useful when one validates a pair of datasets for the first time. For the automation part, QCData is used to compare 2 datasets and QCDir is used to compare datasets in the production directory against their corresponding datasets in the QC directory. Also introduced is ampSYSINFO, a powerful, and extremely useful macro variable which holds a value that summarizes the result of a comparison. Combining Reports into a Single File Deliverable In daily operations of a Biostatistics and Statistical Programming department, we are often tasked with generating reports in the form of tables, listings, and figures (TLFs). A common setting in the pharmaceutical industry is to develop SASreg code in which individual programs generate one or more TLFs in some standard formatted output such as RTF or PDF with a common look and feel. As trends move towards electronic review and distribution, there is an increasing demand for producing a single file as the final deliverable rather than sending each output individually. Various techniques have been presented over the years, but they typically require post-processing individual RTF or PDF files, require knowledge base beyond SAS, and may require additional software licenses. The use of item stores as an alternative has been presented more recently. Using item stores, SAS stores the data and instructions used for the creation of each report. Individual item stores are restructured and replayed at a later time within an ODS sandwich to obtain a single file deliverable. This single file is well structured with either a hyperlinked Table of Contents in RTF or properly bookmarked PDF. All hyperlinks and bookmarks are defined in a meaningful way enabling the end user to easily navigate through the document. This Hands-on-Workshop will introduce the user to creating, replaying, and restructuring item stores to obtain a single file containing a set of tables, listings, and figures. The use of ODS is required in this application using SAS 9.4 in a Windows environment. Getting your Hands on Contrast and Estimate Statements Many SAS users are familiar with modeling with and without random effects through PROC GLM, PROC MIXED, PROC GLIMMIX, and PROC GENMOD. The parameter estimates are great for giving overall effects but analysts will need to use CONTRAST and ESTIMATE statement for digging deeper into the model to answer questions such as: ldquoWhat is the predicted value of my outcome for a given combination of variablesrdquo ldquoWhat is the estimated difference between groups at a given time pointrdquo or ldquoWhat is the estimated difference between slopes for two of three groupsrdquo This HOW will provide a step by step introduction so that the SAS USER will get more comfortable programming ESTIMATE and CONTRAST statements and finding answers to these types of questions. The hands on workshop will focus on statements that can be applied to either fixed effects models or mixed models. Advanced Programming Techniques with PROC SQL Kirk Paul Lafler The SQL Procedure contains a number of powerful and elegant language features for SQL users. This hands-on workshop (HOW) emphasizes highly valuable and widely usable advanced programming techniques that will help users of Base-SAS harness the power of the SQL procedure. Topics include using PROC SQL to identify FIRST. row, LAST. row and Between. rows in BY-group processing constructing and searching the contents of a value-list macro variable for a specific value data validation operations using various integrity constraints data summary operations to process down rows and across columns and using the MSGLEVEL system option and METHOD SQL option to capture vital processing and the algorithm selected and used by the optimizer when processing a query. How to analyze correlated and longitudinal data United States Food and Drug Administration (FDA) requires an annotated Case Report Form (aCRF) to be submitted as part of the electronic data submission for every clinical trial. aCRF is a PDF document that maps the captured data in a clinical trial to their corresponding variable names in the Study Data Tabulation Model (SDTM) datasets. The SDTM Metadata Submission Guidelines recommends that the aCRF should be bookmarked in a specific way. A one-to-one relationship between the bookmarks and aCRF forms is not typical one form may have two or more bookmarks. Therefore, the number of bookmarks can easily reach thousands in any study Generating the bookmarks manually is a tedious, time consuming job. This paper presents an approach to automate the entire bookmark generation process by using SASreg 9.2 and later releases, Ghostscript, a PDF editing tool, and leveraging the linkages between forms and their corresponding visits. This approach could potentially save tremendous amounts of time and the eyesight of programmers while reducing the potential for human error. Did the Protocol Change Work Interrupted Time Series Evaluation for Health Care Organizations. Carol Conell and Alexander Flint Background: Analysts are increasingly asked to evaluate the impact of policy and protocol changes in healthcare, as well as in education and other industries. Often the request occurs after the change is implemented and the objective is to provide an estimate of the effect as quickly as possible. This paper demonstrates how we used time series models to estimate the impact of a specific protocol change using data from the electronic health record (EHR). Although the approach is well established in econometrics, it remains much less common in healthcare: the paper is designed to make this technique available to intermediate level SAS programmers. Methods: This paper introduces the time series framework, terminology, and advantages to users with no previous experience using time series. It illustrates how SAS ETS can be used to fit an interrupted time series model to evaluate the impact of a one-time protocol change based on a real-world example from Kaiser Northern California. Macros are provided for creating a time series database, fitting basic ARMA models using PROC ARIMA, and comparing models. Once the simple time-series structure is identified for this example, heterogeneity in the effect of the intervention is examined using data from subsets of patients defined by the severity of their presentation. This shows how the aggregated approach can allow exploring effect heterogeneity. Conclusions: Aggregating data and applying time series methods provides a simple way to evaluate the impact of protocol changes and similar interventions. When the timing of these interventions is well-defined, this approach avoids the need to collect substantial data on individual level confounders and problems associated with selection bias. If the effect is immediate, the approach requires a very moderate number of time points. Finding Strategies for Credit Union Growth without Mergers or Acquisitions In this era of mergers and acquisitions, community banks and credit unions often believe that bigger is better, that they cant survive if they stay small. Using 20 years of industry data, we disprove that notion for credit unions, showing that even small ones can grow slowly but strongly on their own, without merging with larger ones. We first show how we find this strategy in the data. Then we segment credit unions by size and see how the strategy changes within each segment. Finally, we track the progress of these segments over time and develop a predictive model for any credit union. In the process, we introduce the concept of quotHigh-Performance Credit Unions, quot which do actions that are proven to lead to credit union growth. Code snippets will be shown for any version of SASreg but will require the SASSTAT package. A Case of Retreatment ndash Handling Retreated Patient Data Sriramu Kundoor and Sumida Urval In certain clinical trials, if the study protocol allows, there are scenarios where subjects are re-enrolled into the study for retreatment. As per CDISC guidelines these subjects need to be handled in a manner different from non-retreated subjects. The CDISC SDTM Implementation Guide versions 3.1.2 (Page 29) and 3.2 (Section 4 - page 8) state: ldquoThe unique subject identifier (USUBJID) is required in all datasets containing subject-level data. USUBJID values must be unique for each trial participant (subject) across all trials in the submission. This means that no two (or more) subjects, across all trials in the submission, may have the same USUBJID. Additionally, the same person who participates in multiple clinical trials (when this is known) must be assigned the same USUBJID value in all trials. rdquo Therefore a retreated subject cannot have two USUBJIDs in spite of being the same person undergoing the trial phase more than once. This paper describes (with suitable examples) a method of handling retreated subject data in the SDTMs as per CDISC standards, and at the same time capturing it in such a way that it is easy for the programmer or statistician to analyze the data in ADaM datasets. This paper also discusses the conditions that need to be followed (and the logic behind them) while programming retreated patient data into the different SDTM domains. Why and What Standards for Oncology Studies (Solid Tumor, Lymphoma and Leukemia) Each therapeutic area has its own unique data collection and analysis. Oncology especially, has particularly specific standards for collection and analysis of data. Oncology studies are also separated into one of three different sub types according to response criteria guidelines. The first sub type, Solid Tumor study, usually follows RECIST (Response Evaluation Criteria in Solid Tumor). The second sub type, Lymphoma study, usually follows Cheson. Lastly, Leukemia study follows study specific guidelines (IWCLL for Chronic Lymphocytic Leukemia, IWAML for Acute Myeloid Leukemia, NCCN Guidelines for Acute Lymphoblastic Leukemia and ESMO clinical practice guides for Chronic Myeloid Leukemia). This paper will demonstrate the notable level of sophistication implemented in CDISC standards, mainly driven by the differentiation across different response criteria. The paper will specifically show what SDTM domains are used to collect the different data points in each type. For example, Solid tumor studies collect tumor results in TR and TU and response in RS. Lymphoma studies collect not only tumor results and response, but also bone marrow assessment in LB and FA, and spleen and liver enlargement in PE. Leukemia studies collect blood counts (i. e. lymphocytes, neutrophils, hemoglobin and platelet count) in LB and genetic mutation as well as what are collected in Lymphoma studies. The paper will also introduce oncology terminologies (e. g. CR, PR, SD, PD, NE) and oncology-specific ADaM data sets - Time to Event (--TTE) data set. Finally, the paper will show how standards (e. g. response criteria guidelines and CDISC) will streamline clinical trial artefacts development in oncology studies and how end to end clinical trial artefacts development can be accomplished through this standards-driven process. Efficacy Endpoint Analysis Dataset Generation with Two-Layer ADaM Design Model In clinical trial data processing, the efficacy endpoints dataset design and implementation are often the most challenging process to standardize. This paper introduces a two-layer ADaM design method for generating an efficacy endpoints dataset and summarizes the practices from past projects. The two-layer ADaM design method improves not only implementation and review, but validation as well. The method is illustrated with examples. Strategic Considerations for CDISC Implementation Amber Randall and Bill Coar The Prescription Drug User Fee Act (PDUFA) V Guidance mandates eCTD format for all regulatory submissions by May 2017. The implementation of CDISC data standards is not a one-size-fits-all process and can present both a substantial technical challenge and potential high cost to study teams. There are many factors that should be considered in strategizing when and how which include timeline, study team expertise, and final goals. Different approaches may be more efficient for brand new studies as compared to existing or completed studies. Should CDISC standards be implemented right from the beginning or does it make sense to convert data once it is known that the study product will indeed be submitted for approval Does a study team already have the technical expertise to implement data standards If not, is it more cost effective to invest in training in-house or to hire contractors How does a company identify reliable and knowledgeable contractors Are contractors skilled in SAS programming sufficient or will they also need in-depth CDISC expertise How can the work of contractors be validated Our experience as a statistical CRO has allowed us to observe and participate in many approaches to this challenging process. What has become clear is that a good, informed strategy planned from the beginning can greatly increase efficiency and cost effectiveness and reduce stress and unanticipated surprises. SDD project management tool real-time and hassle free ---- a one stop shop for study validation and completion rate estimation Do you feel sometimes it is like an octopus to work on multiple projects as a lead program or it is hard to monitor whatrsquos going on Perhaps you know about Murphyrsquos Law: Anything that can go wrong will go wrong. And you will want to be the first one to know it before anybody else. Whatrsquos its impact and whatrsquos the downstream process After pulling the study submission package up to SDD, we developed a working process which collects status information of each program and output. Then a SAS program will read in the status report of repository documents and update the tracker with bull Timestamp (last modified, last run) of: o Source and validation program. o Upstream documents (served as input of the program such as raw data or macros). o Downstream documents Features including bull Pinnacle 21 traffic lighting bull Pulling time variables from SDD and building the logic (rawltSDTMltADaM, SourceltValidation) bull Logscan in batch (time estimation on completion) bull Metadata level checking bull The workflow of all these above bull Scheduled job of running the sequenced above tasks bull Study completion report (and algorithm) Building Better ADaM Datasets Faster With If-Less Programming One of major tasks in building ADaM datasets is to write the SAS code to implement the ADaM variables based on an ADaM specification. SAS programmers often find this task tedious, time-consuming and even prone to error. The main reason that the task seems daunting is because a large number of variables have to be created with if-then-else statements in one or more data steps at the same time for each of ADaM datasets. In order to address this common issue and alleviate the process involved, this paper introduces a small set of data step inline macros that allow programmers to derive most of ADaM variables without using if-then-else statements. With this if-less programming approach, a programmer can not only make a piece of ADaM implementation code easy to read and understand, but also makes it easy to modify along with the evolving ADaM specification, and straight to reuse in the development of other ADaM datasets, or studies. Whatrsquos more, this approach can be applied to the derivation of ADaM datasets from both SDTM, and non-SDTM datasets. Whatrsquos Hot ndash Skills for SASreg Professionals Kirk Paul Lafler As a new generation of SASreg user emerges, current and prior generations of users have an extensive array of procedures, programming tools, approaches and techniques to choose from. This presentation identifies and explores the areas that are hot in the world of the professional SAS user. Topics include Enterprise Guide, PROC SQL, PROC REPORT, Output Delivery System (ODS), Macro Language, DATA step programming techniques such as arrays and hash objects, SAS University Edition software, technical support at support. sas, wiki-content on sasCommunity. orgreg, published ldquowhiterdquo papers on LexJansen, and other venues. Creating Dynamic Documents with SASreg in the Jupyter Notebook to Reinforce Soft Skills Experience with technology and strong computing skills continue to be among the most desired qualifications by employers. Programs in Statistics and other especially quantitative fields have bolstered the programming and software training they impart on graduates. But as these skills become more common, there remains an equally important desire for what are often called quotsoft skillsquot: communication, telling a story, extracting meaning from data. Through the use of SASreg in the Jupyter Notebook traditional programming assignments are easily transformed into exercises involving both analytics in SAS and writing a clear report. Traditional reports become dynamic documents which include both text and living SAS reg code that gets run during document creation. Students should never just be writing SAS reg code again. Contributing to SASreg By Writing Your Very Own Package One of the biggest reasons for the explosive growth of R statistical software in recent years is the massive collection of user-developed packages. Each package consists of a number of functions centered around a particular theme or task, not previously addressed (well) within the software. While SAS reg continues to advance on its own, SAS reg users can now contribute packages to the broader SAS reg community. Creating and contributing a package is simple and straightforward, empowering SAS reg users immensely to grow the software themselves. There is a lot of potential to increase the general applicability of SAS reg to tasks beyond statistics and data management, and its up to you Collaborations in SAS Programming or Playing Nicely with Others Kristi Metzger and Melissa R. Pfeiffer SAS programmers rarely work in isolation, but rather are usually part of a team that includes other SAS programmers such as data managers and data analysts, as well as non-programmers like project coordinators. Some members of the team -- including the SAS programmers -- may work in different locations. Given these complex collaborations, it is increasingly important to adopt approaches to work effectively and easily in teams. In this presentation, we discuss strategies and methods for working with colleagues in varied roles. We first address file organization -- putting things in places easily found by team members -- including the importance of numbering programs that are executed sequentially. While documentation is often a neglected activity, we next review the importance of documenting both within SAS and in other forms for the non-SAS users of your team. We also discuss strategies for sharing formats and writing friendly SAS coding for seamless work with other SAS programmers. Additionally, data sets are often in flux, and we talk about approaches that add clarity to data sets and their production. Finally, we suggest tips for double-checking another programmerrsquos code andor output, including the importance of confirming the logic behind variable construction and the use of proc compare in the confirmation process. Ultimately, adopting strategies that ease working jointly helps when you have to review work you did in the past and makes for a better playground experience with your teammates. A Brief Introduction to WordPress for SAS Programmers WordPress is a free, open-source platform based on PHP and MySQL used to build websites. It is easy to use with a point-and-click user interface. You can write custom HTML and CSS if you want, but you can also build beautiful webpages without knowing anything at all about HTML or CSS. Features include a plugin architecture and a template system. WordPress is used by more than 26.4 of the top 10 million websites as of April 2016. In fact, SASreg blogs (hosted at blogs. sas) use the wordPress platform. If you are considering starting a blog to share your love of SAS or to raise the profile of your business and are considering using WordPress, join us for a brief introduction to WordPress for SAS programmers. How to Be a Successful and Healthy Home-Based SAS Programmer in PharmaBiotech Industry Abstract Submission 10 min. Quick Tip Talk WUSS 2016 Educational Forum and Conference September 7-9, 2016 Grand Hyatt San Francisco on Union Square San Francisco, California How to Be a Successful and Healthy Home-Based SAS Programmer in PharmaBiotech Industry Daniel Tsui Parexel International Inc. With the advancement of technology, the tech industry accepts more and more flexible schedules and telecommuting opportunities. In recent years, more statistical SAS programming jobs in PharmaBiotech industry have shifted from office-based to home-based. There has been ongoing debates about how beneficial is the shift. A lot of room is still available for discussion about the pros and cons of this home-based model. This presentation is devoted to investigate these pros and cons as home-based SAS programmer within the pharmabiotech industry. The overall benefits have been proposed in a Microsoft whitepaper based on a survey, Work without Walls, which listed the top 10 benefits of working from home from the employee viewpoint, such as workhome balance, avoid traffic, more productive, less distractions, etc. However, to be a successful home-based SAS programmer in the pharmabiotech industry, some enemies have to be defeated, such as 24 hours on call, performance issues, solitude, advancement opportunities, dealing with family, etc. This presentation will discuss some key highlights. Lora Delwiche and Susan Slaughter SAS Studio is an important new interface for SAS, designed for both traditional SAS programmers and for point-and-click users. For SAS programmers, SAS Studio offers many useful features not found in the traditional Display Manager. SAS Studio runs in a web browser. You write programs in SAS Studio, submit the programs to a SAS server, and the results are returned to your SAS Studio session. SAS Studio is included in the license for Base SAS, is the interface for SAS University Edition and is the default interface for SAS OnDemand for Academics. Both SAS University Edition and SAS OnDemand for Academics are free of charge for non-commercial use. With SAS Studio becoming so widely available, this is a good time to learn about it. An Animated Guide: An introduction to SAS Macro quoting This cartoon like presentation expands materials in a previous paper (that explained how SAS processes Macros) to show how SAS processes macro quoting. It is suggested that the quotmap of the SAS Supervisorquot in this cartoon is a very useful paradigm for understanding SAS macro quoting. Boxes on the map are either subroutines or storage areas and the cartoon allows you to see quotquotedquot tokens flow through the components of the SAS supervisor as code executes. Basic concepts for this paper are: 1) the map of the SAS supervisor 2) the idea that certain parts of the map monitor tokens as they pass through 3) the idea of SAS tokens as rule triggers for actions to be taken by parts of the map 4) macro masking prevents recognition of tokens and the triggering of rules 5) the places in the SAS system where unquoting happens. Principles of equal-channel angular pressing as a processing tool for grain refinement Ruslan Z. Valiev a. 1. , Terence G. Langdon b. C. . . a Institute of Physics of Advanced Materials, Ufa State Aviation Technical University, Ufa 450000, Russian Federation b Materials Research Group, School of Engineering Sciences, University of Southampton, Southampton SO17 1BJ, UK c Departments of Aerospace amp Mechanical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089-1453, USA Received 16 January 2006, Accepted 20 February 2006, Available online 24 April 2006During the last decade, equal-channel angular pressing (ECAP) has emerged as a widely-known procedure for the fabrication of ultrafine-grained metals and alloys. This review examines recent developments related to the use of ECAP for grain refinement including modifying conventional ECAP to increase the process efficiency and techniques for up-scaling the procedure and for the processing of hard-to-deform materials. Special attention is given to the basic principles of ECAP processing including the strain imposed in ECAP, the slip systems and shearing patterns associated with ECAP and the major experimental factors that influence ECAP including the die geometry and pressing regimes. It is demonstrated that all of these fundamental and experimental parameters play an essential role in microstructural refinement during the pressing operation. Attention is directed to the significant features of the microstructures produced by ECAP in single crystals, polycrystalline materials with both a single phase and multi-phases, and metalndashmatrix composites. It is shown that the formation of ultrafine grains in metals and alloys underlies a very significant enhancement in their mechanical and functional properties. Nevertheless, it is demonstrated also that, in order to achieve advanced properties after processing by ECAP, it is necessary to control a wide range of microstructural parameters including the grain boundary misorientations, the crystallographic texture and the distributions of any second phases. Significant progress has been made in the development of ECAP in recent years, thereby suggesting there are excellent prospects for the future successful incorporation of the ECAP process into commercial manufacturing operations. Entsprechender Autor. Address: Departments of Aerospace amp Mechanical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089-1453, USA. Tel. 44 2380 5947721 213 740 0491 fax: 44 2380 5930151 213 740 8071. 1 Tel. fax: 7 3472 733422. Copyright copy 2006 Elsevier Ltd. All rights reserved.