Statistics for Clinical Programmers
Understand the statistical decisions your code operationalizes
Implement common clinical analyses faithfully, diagnose model problems and explain the result path to reviewers.
Estimands, analysis populations, validated ADaM and statistical methods
Verified model results, diagnostics, sensitivity analyses and analysis audit trail
Real course content
6 chapters
Each chapter contains instructional sections, a CLIN-301 worked workflow, failure modes, a concrete deliverable, and an assessment.
Analysis populations & estimands
ITT/mITT/PP/safety, the estimand framework (ICH E9 R1), intercurrent events, and choosing the analysis set.
Chapter 1 of 6
Descriptive stats & CIs done right
Summary statistic conventions, confidence intervals, proportions and rates, presentation vs computation precision.
Chapter 2 of 6
Common efficacy models
ANCOVA, MMRM, logistic regression, CMH — and reading LSMEANS/estimate output.
Chapter 3 of 6
Survival analysis
Kaplan–Meier, log-rank, Cox proportional hazards and hazard ratios, and censoring rules in practice.
Chapter 4 of 6
Multiplicity & interim analyses
Type-I error and alpha spending, hierarchical testing, interim looks, and programming the gatekeeping.
Chapter 5 of 6
Missing data & sensitivity analyses
Missingness mechanisms, imputation approaches (LOCF, MI, tipping point), and implementing sensitivity analyses.
Chapter 6 of 6