Chapter 4 Transforming Data and Creating Analysis Data Sets

Key Concepts for Creating Analysis Data Sets

Defining Variables Once

Defining Study Populations

Defining Baseline Observations

Last Observation Carried Forward (LOCF)

Defining Study Day

Windowing Data

Transposing Data

Categorical Data and Why Zero and Missing Results  Differ Greatly

Performing Many-to-Many Comparisons/Joins

Using Medical Dictionaries

Other Tricks and Traps in Data Manipulation

Common Analysis Data Sets

Subject Level Analysis Data Set

Change-from-Baseline Data Set

Time-to-Event Data Set

 

Once the “raw” clinical data have been imported into SAS, the next step is to transform those raw data into more useful analysis-ready data. The raw data source could befrom a clinical ...

Get SAS Programming in the Pharmaceutical Industry, Second Edition, 2nd Edition now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial