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Your SAS Secrets Exposed! [4 SAS Tips]

Clinical Programming Team

SAS_Tips

During my time in the life science industry I have learnt a lot of SAS techniques through attending training sessions, however some of the best SAS tips I have picked up were from other programmers: for instance when asking for advice on a coding problem or running programs written by colleagues. I found there are many simple SAS® tips you can use in your day to day SAS programming. This blog will provide explanations and examples of four of these.

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Topics: Clinical Programming, SAS Programming, SAS Datasets, SAS Macros, SAS

Is Multiple Imputation in Clinical Trials Worth the Effort?

Statistical Consultancy Team

In a case study examined to look at Multiple Imputation (MI) in clinical trials, comparing Active to Placebo treatment (at Weeks 2, 4, 6 and 12 of the trial) in adolescents with acne, drop outs were common.  The primary endpoint was the number of lesions at Week 12.  The factors believed to affect the propensity to be missing included age, side effects and lack of efficacy, and thus missing data patterns differ between groups. 

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Topics: Statistical Programming, FDA, SAS Programming, Statisticians in the Pharmaceutical Industry (PSI), Propensity Scoring, Multiple Imputation

Efficient Data Reviews and Quality in Clinical Trials [Video]

Statistical Consultancy Team

This video is presented by Kelci Miclaus from SAS JMP who was a speaker at Clinical Data Live 2013. Her presentation was is titled: 'Efficient Data Reviews and Quality in Clinical Trials'.

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Topics: Bayesian Statistics, Randomization, Serious Adverse Events (SAEs), Quality Control, CDISC, SAS Programming, Remote Monitoring, On-Site Monitoring, FDA, Visualization, Standardization, Remote Data Capture, Source Data Verification (SDV), Additional Monitoring, Efficient Data Review, Fraud Detection, Patient Safety

Using Microsoft Excel to write SAS code in Clinical Trials

Clinical Programming Team

Often when we write SAS code in the pharmaceutical industry, there is a high level of repetition. This guide explains ways of writing repetitive SAS code using Excel that will reduce the overall time to write the code and make large scale amendments easier and quicker.

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Topics: Clinical Programming, SAS Programming, Large Datasets, SAS Datasets

How to Deal with Large SAS Datasets in Clinical Trials

Clinical Programming Team

 

This slideshow focuses on the problems faced when working with large SAS datasets and ways to resolve these problems.

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Topics: Clinical Data Storage, Clinical Programming, SAS Programming, Large Datasets, SAS Datasets

Overview of Receiver Operating Characteristic (ROC) Curves in SAS

Statistical Consultancy Team

The repeated dosing of some drugs can induce injury to the human liver. Regular monitoring of biomarkers assayed in blood samples may help to diagnose safety issues sooner. There is interest in developing new biomarkers that are more specific than the standard tests [e.g. Alanine Transaminase (ALT)] commonly used. 

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Topics: Statistical Programming, SAS Programming, Biomarkers, ROC Analysis, ROC Curve

Multiple Forest Plots and the use of SAS Macros

Clinical Programming Team

 

This blog post discusses the SAS/Graph Annotation option and how this can be used in combination with SAS Macros to allow the creation of multiple Forest Plots, giving details on what can and cannot be controlled as part of the macro call. The purpose of this paper is to highlight the methods of using the ANNOTATE Option available with the SAS/GRAPH procedures for producing a Forest Plot using the SAS system and how this can be adapted to allow multiple plots to be created using SAS Macros.

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Topics: Randomization, SAS Programming, SAS Macros, SAS Forest Plots

SAS Proc Transpose VS SAS Arrays in Clinical Programming

Clinical Programming Team

Sometimes you may need to restructure your dataset to move data from columns into rows, and vice versa. Two methods for doing this are using the transpose procedure or creating an array.

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Topics: Clinical Programming, SAS Programming, SAS Proc Transpose, SAS Arrays

SAS Business Intelligence - A Perceptive Vision in Clinical Trials

Clinical Programming Team
Tables, listings and figures are part of day to day clinical submissions. Hence it would be useful if statisticians/clients could easily analyse the study data through different time points. This allows for better decisions because you are able to view outputs while the study is on going, this visualisation during a study can allow for great efficiencies as decisions can be made earlier in the clinical trial.
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Topics: Statistical Programming, CDISC SDTM, SDTM Domains, CDISC, SAS Programming, SDTM, PK Analysis, Visualization, SAS BI, SAS Business Intelligence, SAS Web Report Studio

PK data analysis and reporting: SAS or Phoenix WinNonlin?

Statistical Consultancy Team

SAS [1] is regarded as the industry standard for clinical data analysis and reporting; however, Phoenix WinNonlin[2], which is a powerful tool tailored to the specific demands of PK data, is widely accepted as the industry standard for PK data analyses. This widespread separation of responsibilities, and the resultant transfer of data between software applications (and in many cases departments), has the potential to not only cause significant reporting delays, but can also lead to incohesive clinical study reports (CSRs) containing tables, listings and figures (TLFs) derived from various different sources.

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Topics: Pharmacokinetics and Pharmacodynamic, Clinical Study Report, SAS Programming, Biostatistics Consulting, PK Analysis

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