Clinical SAS Programming Course

Lesson 1 • SAS Interface and Navigation Basics

Covers the SAS windowing environment, log interpretation, and program submission. Establishes the workspace foundation needed for all subsequent programming tasks.

Lesson 2 • SAS Data Step Fundamentals

Introduces the DATA step execution model, variable creation, and basic logic. Forms the programming backbone for all clinical data manipulation tasks ahead.

Lesson 3 • SAS Libraries and File Management

Teaches LIBNAME statements, SAS datasets, and directory structures for clinical projects. Proper file management is critical for reproducible and audit-ready programming.

Lesson 4 • Reading and Importing Clinical Data

Covers INFILE, PROC IMPORT, and reading delimited and fixed-width files. Prepares students to ingest raw clinical data from multiple source formats.

Lesson 1 • Date and Time Handling in Clinical Data

Covers SAS date functions, formats, and interval calculations critical to clinical timelines. Accurate date derivations underpin exposure calculations and adverse event timing.

Lesson 2 • Character and Numeric Functions

Teaches string manipulation, numeric rounding, and conversion functions used in data cleaning. These functions resolve common data quality issues found in clinical source data.

Lesson 3 • Merging and Combining Datasets

Covers SET, MERGE, and BY-group processing for combining clinical datasets. Accurate merging is essential for linking subject demographics, visits, and lab data.

Lesson 4 • Subsetting and Filtering Records

Teaches WHERE, IF, and DELETE statements for selecting relevant clinical records. Precise subsetting ensures analysis populations match protocol-defined criteria.

Lesson 1 • PROC SQL Syntax and Query Structure

Introduces SELECT, FROM, WHERE, and ORDER BY clauses in the SAS SQL environment. Establishes query-writing skills that underpin all subsequent SQL-based clinical tasks.

Lesson 2 • Subqueries and Correlated Queries

Introduces inline views, subqueries, and correlated queries for complex data retrieval. These techniques solve multi-step derivation problems common in ADaM dataset creation.

Lesson 3 • Joining Clinical Tables with SQL

Covers INNER, LEFT, RIGHT, and FULL joins for linking clinical domain tables. Joining is the primary method for assembling analysis datasets from multiple SDTM domains.

Lesson 4 • Creating and Managing Tables with SQL

Covers CREATE TABLE, INSERT, and macro variable creation via SQL. Enables programmers to build persistent datasets and pass values into macro-driven programs.

Lesson 1 • Building a Clinical Macro Library

Covers AUTOCALL libraries, stored compiled macros, and macro catalogue management. A shared macro library standardises programming across a clinical team or organisation.

Lesson 2 • Macro Utilities and Debugging

Introduces %PUT, MPRINT, MLOGIC, and SYMBOLGEN options for macro troubleshooting. Debugging skills are essential for validating macro behaviour in regulated clinical environments.

Lesson 3 • Macro Variables and Substitution

Introduces %LET, automatic macro variables, and symbolic substitution in SAS code. Macro variables eliminate hard-coded values and enable flexible, study-agnostic programs.

Lesson 4 • Writing and Calling SAS Macros

Covers %MACRO, %MEND, parameters, and macro invocation syntax. Encapsulating logic in macros is the standard approach for reusable clinical reporting utilities.

Lesson 1 • Special Purpose and Relationship Datasets

Introduces SUPPQUAL, RELREC, and trial design datasets required for complete submissions. These datasets capture data that cannot fit standard domain structures.

Lesson 2 • SDTM Model and Domain Architecture

Explains the SDTM general observation classes, domain structure, and controlled terminology. Understanding the model is prerequisite to correctly mapping any raw clinical variable.

Lesson 3 • Mapping Raw Data to SDTM Domains

Covers the annotation process, SDTM mapping specifications, and variable derivation rules. Accurate mapping ensures traceability from raw source data to submission datasets.

Lesson 4 • Key SDTM Domains: DM, AE, CM, LB

Builds DM, AE, CM, and LB domains with correct variable derivations and timing variables. These four domains represent the most commonly reviewed datasets in regulatory submissions.

Lesson 1 • ADaM Model Principles and Structures

Explains ADaM dataset classes, traceability requirements, and the one-procedure-one-dataset principle. ADaM design decisions directly affect the validity of statistical outputs.

Lesson 2 • BDS Datasets for Continuous Endpoints

Builds ADLB and ADVS using the basic data structure with AVAL, BASE, and CHG variables. BDS datasets support the primary and secondary efficacy and safety analyses.

Lesson 3 • OCCDS Datasets for Events and Medications

Creates ADAE and ADCM using the occurrence data structure for event-level analysis. OCCDS datasets support adverse event summaries and exposure-adjusted incidence calculations.

Lesson 4 • Building the Subject-Level Dataset ADSL

Covers derivation of treatment flags, population flags, and baseline characteristics in ADSL. ADSL is the backbone dataset referenced by all other ADaM domain datasets.

Lesson 1 • Figures and Graphs for Clinical Reports

Introduces PROC SGPLOT and PROC SGPANEL for producing clinical-grade figures. Graphs visualise efficacy trends, safety profiles, and subject disposition in study reports.

Lesson 2 • Output Delivery System Destinations

Teaches ODS RTF, PDF, Excel, and HTML destinations for clinical output delivery. Selecting the correct ODS destination ensures outputs meet sponsor and agency formatting requirements.

Lesson 3 • Producing Clinical Listings

Covers PROC PRINT, PROC REPORT, and pagination options for subject-level data listings. Listings provide the raw data evidence supporting summary table conclusions.

Lesson 4 • Advanced PROC REPORT Techniques

Covers COMPUTE blocks, LINE statements, and conditional formatting for complex tables. These techniques produce the multi-level summaries required in clinical study reports.

Lesson 1 • Handling Protocol Deviations and Data Issues

Teaches strategies for flagging protocol deviations, handling data queries, and documenting assumptions. Transparent handling of data issues protects submission integrity and reviewer trust.

Lesson 2 • Double Programming and Independent Validation

Covers the independent validation model, comparison tools, and discrepancy resolution workflows. Double programming is the primary quality assurance method in clinical SAS programming.

Lesson 3 • Programming Standards and Style Guides

Introduces naming conventions, code commenting, and sponsor-specific programming standards. Consistent standards reduce errors and enable efficient cross-programmer code review.

Lesson 4 • Submission Package Preparation

Covers the technical specifications for electronic submission packages including datasets and metadata. Correct package assembly is required for regulatory agency acceptance of submissions.