The Simplest Guide to Clinical Trials Data Analysis with SAS

Explore clinical study documents used in trials and how clinical programmers build analysis and reporting data sets. Understand data types, constraints, and why these documents matter for the study phase.

Identify key study documents guiding SAS programming in clinical trials, with the protocol as the central plan. See how sponsors and IRBs review safeguards for participant safety and study integrity.

Understand how informed consent supports ethical clinical research by presenting patients with study details, risks, benefits, experimental and standard treatments, and voluntary participation.

Identify who can participate in a clinical trial by applying the inclusion criteria and who is excluded by the exclusion criteria, considering age, gender type, and disease stage.

Review the importance and purpose of clinical study documents and how they support the analysis and reporting of clinical trials.

Power up your SAS session to work hands-on with clinical data, build derived and analysis datasets, and generate the final clinical study reports for submission to regulatory authorities.

Learn how to access SaaS on demand for academics, create a user profile, verify your account, and launch the SaaS studio session to begin programming.

Learn how to transform CRF data into derived datasets and import them into SAS to generate the demographics table, a foundational clinical study report.

Construct a demographics table from raw data by linking age, sex, race, and TT to derive variables, producing a SAS-driven clinical study report table with placebo and active groups.

Plan and program the demographics table in SAS by converting raw data to derived datasets, mapping CRF fields for age, gender, and race.

Upload the demographics Excel file into SAS Studio and import it with the import wizard, creating the demo dataset in the work library with variables as column names; check log.

apply proc means to generate n, mean, standard deviation, min and max for age, and use proc freq for counts and percentages of sex and race.

Derive the age variable from day, month, and year of birth to date of diagnosis, using sas data steps to build dob and generate final demography statistics.

Compute age summary statistics (n, mean, standard deviation, min, max) with proc means from Demo1. Export results to a dataset, sort by TRT, and obtain placebo and active group statistics.

Add a third treatment group in the demo data by using explicit output with TRT=2, enabling summary statistics for placebo, active, and all patients via proc means.

Derive a sex variable from gender by using proc format to map 1 to male and 2 to female, then apply it with put to create males or females.

Derive the race variable by creating a custom format and mapping codes to categories such as white, black, Hispanic, Asian, and other, then generate race C using a put function.

Fix precision points and missing parentheses in SAS code by applying conditional formatting: N zero decimals, mean/min/max one decimal, std two decimals, and increase value length.

Explore transposing a vertically arranged all stats dataset into a three-column table using proc transpose, aligning placebo, active, and overall patients with by, id, and underscore prefix options.

Use proc report on the transposed dataset to generate final report, with columns and define statements, no print for vars, display for stack and treatment groups, and sort by order.

Refine a SAS clinical trial report by using Proc SQL and Proc Report to add labels, population counts, macro variables, titles, and footnotes for the final demographics table.