1. Clinical Trial Lifecycle, Protocols & SAP
Phases I-IV clinical development, Protocol reviews, Statistical Analysis Plans (SAP), and CRFs.
Clinical Trial Phases I to IV: Purpose, Sample Size & Endpoints
Understand the four distinct phases of human drug trials and their statistical reporting requirements.
Read Guide →The Statistical Analysis Plan (SAP) & Table Mock Shells
The master roadmap that guides all clinical programming, derivation rules, and TLF formatting.
Read Guide →The Clinical Study Protocol & Informed Consent Architecture
The ethical and operational foundation governing all clinical trials, subject eligibility, and data collection.
Read Guide →2. CDISC Standards: SDTM & ADaM Architecture
Study Data Tabulation Model (SDTM), Analysis Data Model (ADaM), ADSL subject-level derivation, and BDS.
CDISC SDTM vs. ADaM: Core Differences, Domains & Datasets Architecture
The essential guide to CDISC SDTM tabulation datasets (DM, AE, VS, LB) vs. ADaM analysis-ready datasets (ADSL, BDS).
Read Guide →ADSL Subject-Level Analysis Dataset: Derivation Rules & Keys
Master the structure of ADSL, the single most critical dataset in any clinical trial submission.
Read Guide →3. Clinical Tables, Listings & Figures (TLFs)
Production of Demographics Table 14.1.1, Adverse Event tables, Lab shifts, and PROC REPORT styling.
Demographics Table 14.1.1: Complete Step-by-Step SAS Workflow
Build the quintessential clinical trial summary table with continuous and categorical variables.
Read Guide →Adverse Events (AE) Safety Reporting & ADAE Derivations
How to program Treatment-Emergent Adverse Events (TEAEs) and MedDRA hierarchical summary tables.
Read Guide →PROC MEANS & PROC FREQ: Continuous vs. Categorical Clinical Analysis
Master the twin workhorse statistical procedures powering all regulatory clinical CSR deliverables.
Read Guide →SAS Arrays in Clinical Trials: Batch Processing Laboratory Variables
Transform dozens of clinical laboratory parameters (ALT, AST, BILI, ALP, ALBUMIN) using 1-dimensional arrays, DIM bounds, and DO-loops.
Read Guide →SAS vs Python for Clinical Programming: Where Each Fits
The same adverse-event count in SAS and pandas, and the CDISC and validation skills that matter more than the language.
Read Guide →🌐 Explore Companion Knowledge Hubs
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