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A Review of the Annual PhUSE 2016 Conference

Clinical Programming Team

A number of team members were able to represent Quanticate at the PhUSE 2016 annual conference in Barcelona. The PhUSE annual conference is an opportunity for programmers and statisticians to both learn from and share cutting edge knowledge with the pharmaceutical industry. This year, Quanticate presented on producing high quality SAS graphics using the advanced Graphical Template Language (GTL) to bring individual plots together to aid analysis without sacrificing any aesthetical properties in the process. Conference attendees were spoilt for choice with approximately 5 simultaneous presentations every half-hour across 15 streams in total. Here are some interesting presentations which the team enjoyed over the conference duration.

 

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Topics: Programming R, Clinical Programming, SAS Programming, Visualization, Conferences, SAS Graph

Creating High Quality Graphics in Clinical SAS Programming

Statistical Consultancy Team

Utilizing the newer SAS graphical procedures such as SGPLOT and SGPANEL rather than the original SAS Graph procedures is becoming more and more popular in statistical programming through their many user friendly utilities, such as overlaying multiple graphics and adding reference lines with ease. However, as with its predecessor, SAS Graph, any requirement for restructure of the graphical elements still proves to be relatively rigid when sticking to these core graphical procedures. This usually results in creating bespoke program code for each figure which undoubtedly takes time and also runs the risk of inconsistencies across figures.

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Topics: Clinical Programming, SAS Programming, SAS Graph, PROC Template

Delayed-entry models using PROC PHREG in Survival Analysis

Statistical Consultancy Team

Time-to-event data often arise in clinical research, and in many cases represent the primary outcome of interest. These data generally represent the elapsed time between a reference time-point (e.g., treatment randomization) and an event of interest (e.g. death, relapse, etc.).

Whereas right censoring is a feature that is easily accommodated by most existing software, the same doesn’t strictly hold for another feature of survival data, left-truncation. In this post we’ll describe what left-truncation is, when it can arise and provide some SAS code that can be used to derive survival estimates and curves. 

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Topics: Survival Analysis, Statistical Programming, SAS Programming, SAS, PROC PHREG

Understanding the Proportional Odds Assumption in Clinical Trials

Statistical Consultancy Team


Ordinal scales are commonly used to assess clinical outcomes; however, the choice of analysis is often sub-optimal.  In 2007, the Optimising Analysis of Stroke Trials (OAST) collaboration showed that ordinal-appropriate analyses of ordinal stroke outcome scales were preferable over binary analysis of a chosen ‘favourable’ outcome[1] but uptake of ordinal methods between 2007 and 2014 has been low [2].

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Topics: Randomization, SAS Programming, Biostatistics Consulting, Statistics, Ordinal Logistic Regression, Proportional Odds Assumption, PROC Logistic, Neurology

Comparing treatment response curves: a practical example in rheumatoid arthritis

Statistical Consultancy Team

Nowadays, more and more studies are being designed to collect information on treatment response at several time points during the treatment period of the study. Although the primary endpoint is often the comparison at the end of the study of the absolute response or of the change to baseline between study treatments, analyses involving intermediate time points in the assessment of treatment effects, e.g., repeated measures modeling, are now widely used.

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Topics: Programming R, SAS Programming, Therapeutic Areas, Biostatistics Consulting, Phase 3 Studies, Rheumatoid Arthritis (RA), PROC NLMIXED, PROC IML

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, CDISC, FDA, Standardization, Remote Monitoring, Remote Data Capture, Source Data Verification (SDV), Randomization, SAS Programming, On-Site Monitoring, Serious Adverse Events (SAEs), Quality Control, Visualization, 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

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