Analyzing Clinical Trial Data

Required Source Documents

• Finalized and signed Statistical Analysis Plan (SAP)
• Protocol (including all amendments)
• TLF shells (mock table/listing/figure specifications)
• SDTM and ADaM datasets with define.xml
• Randomization list (unblinded, post-database lock)
• Protocol deviation log
• Data Management Report (query resolution status, edit checks)
• Medical coding dictionaries (MedDRA version, WHO Drug Dictionary version)

Step 1 — Define and Verify Analysis Populations

Construct each analysis population per the SAP:

• Intent-to-Treat (ITT): All randomized participants analyzed as randomized, regardless of protocol adherence. This is the primary population for superiority trials per ICH E9.
• Modified ITT (mITT): ITT minus participants who never received study drug or have no post-baseline efficacy assessment. Document specific exclusion criteria.
• Per-Protocol (PP): Participants who completed the study without major protocol deviations. Define deviation types that trigger PP exclusion (wrong treatment, insufficient exposure, prohibited medications, missed primary-endpoint assessments).
• Safety population: All participants who received at least one dose of study medication, analyzed as treated (not as randomized).

Reconcile population counts across datasets. Any discrepancy between randomization list and safety/ITT counts requires documentation.

Step 2 — Produce Demographic and Baseline Tables

Generate the CONSORT-required baseline comparison table:

1. Demographics: age (mean, SD, median, range), sex, race/ethnicity (per FDA and NIH reporting requirements), BMI
2. Disease characteristics: diagnosis duration, disease severity scores, prior treatments, relevant biomarkers
3. Stratification factors: verify balance across treatment arms
4. Baseline efficacy measures: values for primary and key secondary endpoints at baseline
5. Present continuous variables as mean (SD) and median (Q1, Q3); categorical variables as n (%)
6. Do not perform hypothesis tests on baseline characteristics (CONSORT guidance) — describe imbalances narratively

Step 3 — Analyze Primary Endpoint

Execute the pre-specified primary analysis exactly as stated in the SAP:

For Continuous Endpoints

• ANCOVA with treatment as fixed effect, stratification factors as covariates, and baseline value as covariate
• MMRM (mixed model for repeated measures) for longitudinal data: treatment, visit, treatment-by-visit interaction, baseline, baseline-by-visit interaction; unstructured covariance
• Report: LS means per group, difference in LS means, 95% CI, p-value

For Binary Endpoints

• CMH (Cochran-Mantel-Haenszel) test stratified by randomization strata
• Logistic regression as sensitivity analysis
• Report: response rates per group, difference in proportions, 95% CI, odds ratio with 95% CI, p-value

For Time-to-Event Endpoints

• Kaplan-Meier estimates with log-rank test (stratified if applicable)
• Cox proportional hazards model for hazard ratio with 95% CI
• Verify proportional-hazards assumption (Schoenfeld residuals, log-log plot)
• Report: median time-to-event per group (with 95% CI), HR, p-value, KM curves at key timepoints

For Count Data / Recurrent Events

• Negative binomial regression or Andersen-Gill model
• Report: rate per group, rate ratio with 95% CI, p-value

Step 4 — Handle Missing Data

Implement the pre-specified missing-data strategy per ICH E9(R1) estimand framework:

1. Primary approach: The method that aligns with the chosen estimand (e.g., MMRM under MAR assumption for treatment-policy estimand)
2. Sensitivity analyses (required — at least two):
   • Tipping-point analysis: Determine how extreme imputed values must be to reverse the conclusion
   • Pattern-mixture model (MNAR): Reference-based imputation (copy-reference, jump-to-reference)
   • Multiple imputation under various assumptions
   • Worst-case / best-case imputation for binary endpoints
3. Supplementary analyses: Complete-case analysis, last-observation-carried-forward (document as sensitivity only — LOCF is no longer acceptable as primary per FDA and EMA guidance)
4. Documentation: Report the number and reasons for missing data by treatment arm and visit

Step 5 — Analyze Secondary Endpoints with Multiplicity Control

Apply the pre-specified multiplicity adjustment strategy:

1. Hierarchical (fixed-sequence) testing: Test secondary endpoints in pre-specified order; stop testing at first non-significant result. This is the most common approach for pivotal trials.
2. Graphical approach (Bretz et al.): Allocate alpha across endpoints with pre-specified propagation rules
3. Hochberg or Holm step-up/step-down: When endpoints are independent or positively correlated
4. Gate-keeping strategies: For families of primary and secondary endpoints

Analyze each secondary endpoint using the same methodology specified for its data type. Report adjusted and nominal p-values.

Step 6 — Conduct Safety Analysis

Analyze the safety population:

Adverse Events

• Summarize by System Organ Class (SOC) and Preferred Term (PT) using the specified MedDRA version
• Present: any AE, drug-related AE, serious AE (SAE), AE leading to discontinuation, AE leading to death
• Tabulate by severity grade (CTCAE v5 or mild/moderate/severe)
• For each PT, report incidence (n, %) per treatment arm — not number of events (one participant with multiple episodes counts once)
• Flag imbalances (≥2% difference between arms or ≥2× rate ratio) for medical review

Laboratory Data

• Shift tables (baseline category → worst post-baseline category)
• Potentially clinically significant (PCS) values: define thresholds per parameter; tabulate n (%) meeting PCS criteria by arm
• Liver safety: apply Hy's Law criteria (ALT >3× ULN + bilirubin >2× ULN without cholestasis); generate eDISH plot

Vital Signs, ECG, Other Safety

• Summarize by visit with change-from-baseline; flag PCS values
• QTc analysis per ICH E14 if applicable

Step 7 — Generate Tables, Listings, and Figures (TLFs)

Produce all TLFs per the pre-approved shells:

• Follow CDISC Analysis Results Metadata (ARM) standards
• Tables: formatted with proper headers, footnotes, population counts (N = per arm), and statistical references
• Listings: participant-level data for SAEs, deaths, discontinuations, protocol deviations, and concomitant medications
• Figures: KM plots, forest plots for subgroups, waterfall plots (oncology), spider plots (tumor response), bar/line charts for PRO scores
• All output must be reproducible from ADaM datasets with documented programs

Checkpoint B — Analysis Review

1. Primary analysis matches the SAP exactly (no unplanned modifications)
2. All analysis populations are correctly derived and counts reconcile
3. Missing-data handling follows the estimand framework with sensitivity analyses
4. Multiplicity adjustment is correctly applied in the specified order
5. Safety tables use correct MedDRA version and incidence-based (not event-based) counting
6. Hy's Law assessment is completed for studies with hepatotoxicity potential
7. All TLFs match the pre-approved shells
8. Subgroup analyses (sex, age, race, region, baseline severity) are conducted for primary endpoint
9. Statistical programs are validated (double-programming or independent QC)
10. Unblinding log confirms no premature unblinding occurred

Quality Audit

• SAP version matches the version referenced in the CSR
• ADaM datasets are CDISC-compliant with submitted define.xml
• All p-values are reported to the appropriate decimal precision (typically 4 decimal places)
• Confidence intervals are consistently 2-sided 95% unless otherwise specified
• KM curves include number-at-risk tables
• Forest-plot subgroup analyses include interaction p-values
• All post-hoc analyses are clearly labeled as exploratory
• No results are presented that are not derivable from the submitted datasets
• All [VERIFY] flags have been resolved or escalated

Guidelines

1. The SAP must be finalized before database lock and unblinding — any changes after unblinding must be documented and justified as pre-specified sensitivity or clearly labeled post-hoc
2. Never change the primary analysis method after seeing the data without regulatory disclosure
3. Use ITT as the primary population for superiority trials; per-protocol as co-primary for non-inferiority
4. LOCF is no longer acceptable as a primary missing-data method — use MMRM or multiple imputation
5. All statistical programs must have independent QC (double-programming or code review)
6. Report effect estimates with confidence intervals, not only p-values — p-values without effect sizes are insufficient
7. Safety analyses are descriptive — hypothesis testing of AE incidence rates is generally inappropriate
8. Apply the CONSORT flow diagram to document participant disposition through analysis populations
9. Mark any deviation from the SAP with [VERIFY] for biostatistics-lead review
10. This skill produces analysis results — interpretation for regulatory submission requires clinical and regulatory team review