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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

The Creation of ADaM Datasets for Pharmacokinetic (PK) Analysis [Video]

Clinical Programming Team

In this recorded presentation a member of the Quanticate's Statistical Programming Team explores the creation of two ADaM datasets; ADPC and ADPP for Pharmacokinetic (PK) Analysis.

 

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Topics: Pharmacokinetics and Pharmacodynamic, CDISC Guidelines, CDISC SDTM, SDTM Domains, CDISC, Clinical Programming, SDTM, CDISC Standards, PK Analysis, ADaM Datasets

An Overview of Efficacy Endpoints in Oncology Studies

Clinical Programming Team

This blog provides an overview of efficacy endpoints in oncology studies. We will focus on RECIST (Response Evaluation Criteria In Solid Tumours).This is a method of assessing how solid tumours change over the course of a study. We will cover the measurements taken for RECIST assessments (Target lesions, Non target lesions, New lesions) and the possible outcomes, (complete response, partial response, stable disease, progressive disease) and what they mean – for example; patient recovered, stayed the same or got worse. We will also cover how visit windows and censoring can be handled. How and why measurement methods are important.  We will cover other endpoints relevant to oncology such as Overall survival, Objective Response Rate, Best overall response, Disease control at X weeks and quality of life.

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Topics: Clinical Programming, Ethics, Oncology

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

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

CDISC SDTM Therapeutic Area Domains - a Rapidly Evolving Standard?

Clinical Programming Team

The CDISC SDTM model is considered the most stable of the CDISC standards: version 3.1.1 of the Implementation Guide (IG) was published in August 2005; 3.1.2 in November 2008 which has remained the accepted standard. However, in this year’s European Interchange, CDISC revealed an aggressive timeline of therapeutic area (TA) SDTM domain development. From slides 17 and 18 of the presentation you can see that 14 new TA domains are intended to be developed in 2012 alone. The overall goal is to standardise efficacy data elements from 57 TAs in 7 years.

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Topics: CDISC SDTM, SDTM Domains, CDISC, Standardization, Clinical Programming, Biomarkers, SDTM, CDISC Standards, Therapeutic Areas

Survival Analysis: Lifetables and Cox Proportional Hazard Model

Clinical Programming Team

Survival models are commonly used in clinical trials as they are designed to perform 'time to event' analyzes on data with censored observations – something that probably all clinical programmers are familiar with. As censored observation in this case we understand a subject who did not experience the defined event during the whole observation period.

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Topics: Survival Analysis, Adverse Events (AEs), Clinical Programming, Cox Proportional Hazard Model

CDISC SDTM v3.1.2 Theory and Application

Clinical Programming Team

A member of the Clinical Programming Team writes about their experience at the CDISC interchange in Brussels held on 11th-12th April 2011.

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Topics: Statistical Programming, CDISC Guidelines, CDISC SDTM, SDTM Domains, CDISC, Standardization, Clinical Programming, SAS Programming, CDISC Interchange

Clinical SAS Programming – Working Efficiently with Large Datasets

Clinical Programming Team

Issues of Large Datasets

Numerous SAS® programmers experience problems when working with large SAS® datasets that have millions of rows, hundreds of columns and are close to the size of a gigabyte or even more.

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

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