1) Developing Protocol + Sample Size

A. Scientific + clinical foundation

• Define study rationale (unmet need, mechanism, prior evidence, feasibility).
• Translate to objectives:
  • Primary objective (one “win condition”)
  • Key secondary objectives (ranked)
  • Exploratory objectives (biomarkers, PROs, substudies)
• Define endpoint strategy
  • Primary endpoint (precise definition, timepoint, ascertainment)
  • Secondary endpoints (hierarchy / multiplicity plan)
  • Safety endpoints (AEs, SAEs, AESIs, lab/vitals/ECG)
  • Endpoint adjudication plan (if needed)

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B. Trial design decisions (core protocol architecture)

• Choose design: parallel RCT / single-arm / crossover / cluster / pragmatic vs explanatory
• Define:
  • Population (inclusion/exclusion, washout, prohibited meds)
  • Study setting (sites, countries, recruitment pathway)
  • Treatment arms (dose, schedule, rescue meds, allowed concomitants)
  • Randomization (ratio, stratification factors, blocking)
  • Blinding (double-blind, double-dummy, open-label + blinded assessor)
  • Visit schedule + windows (procedures per visit)
  • Adherence and compliance measurement plan
  • Stopping rules / discontinuation criteria
  • Safety oversight: DSMB/IDMC charter needs, AE reporting timelines

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C. Statistical framework in the protocol (high-level)

• Define estimand logic (what effect you want: treatment policy vs hypothetical, etc.)
• Specify analysis populations (ITT, PP, Safety) at a high level
• Outline primary model (e.g., logistic / Cox / MMRM / ANCOVA), alpha, CI

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D. Sample size workflow (from endpoint → assumptions → n)

• Select the primary endpoint metric and effect measure:
  • Binary (risk difference/ratio, OR), time-to-event (HR), continuous (mean diff), repeated measures (MMRM)
• Determine assumptions:
  • Control event rate / mean & SD
  • Expected treatment effect (clinically meaningful)
  • Alpha (two-sided vs one-sided), power
  • Allocation ratio (1:1, 2:1)
  • Dropout/non-evaluable inflation
• Special scenarios to account for:
  • Stratification/cluster effects (ICC), multi-center variation
  • Interim analysis (alpha spending)
  • Non-inferiority margin logic (if NI)
  • Multiple primary endpoints / multiplicity
• Write sample size justification narrative (clinically interpretable, defensible)

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E. Operational + compliance deliverables

• Protocol synopsis (one-page)
• Full protocol + schedule of assessments (SoA)
• Informed consent / assent materials
• Investigator brochure or reference safety info
• Randomization plan high-level (details often in SAP / randomization spec)

Outputs

• Final protocol (and amendment plan)
• Sample size report (assumptions + formula/software + inflation)
• Draft “TLF shells” outline (optional but useful early)

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2) Project Set-Up & Data Management

A. Project set-up (operations & governance)

• Build project plan: timelines, milestones, critical path (FPI/LPI/LPLV/DBL/topline/CSR)
• Define roles and RACI: sponsor/CRO/PI/statistician/DM/medical monitor
• Vendor qualification + contracts:
  • EDC, IWRS/RTSM (randomization), central lab, imaging, ePRO, PK lab, safety database
• Site feasibility + selection:
  • Site capability, recruitment, competing trials, staff training needs
• Trial master file (TMF) structure + QC plan
• Training:
  • Protocol training, GCP training, EDC training, safety reporting training

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B. Data management planning (DMP)

• Create Data Management Plan (DMP):
  • Data sources (EDC, labs, devices, ePRO, imaging)
  • Data flow diagrams + transfer specs
  • Data standards (CDISC expectations if applicable)
  • Quality strategy (edit checks, query process, cleaning cycles)
  • Coding plan (MedDRA for AEs, WHO-DD for meds)
  • Reconciliation plans (SAE vs EDC, lab vs EDC)
  • Database lock criteria & checklist

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C. CRF/eCRF + database build

• Build CRF from protocol endpoints and SoA (no “nice-to-have” fields)
• Annotated CRF (mapping to dataset variables)
• EDC build + validations:
  • Edit checks, range checks, visit windows, missing prompts
• UAT (user acceptance testing) with documented cases + sign-off
• Role-based access + audit trail configuration

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D. Data cleaning + ongoing quality

• Medical coding cycles (regular cadence)
• Query management + KPI tracking
• Central monitoring (RBM) checks:
  • Outliers, fraud signals, protocol deviations, missingness patterns
• Data reviews:
  • Monthly listings review, blinded data review meetings (BDRM)

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E. Database lock readiness

• Lock preparation:
  • All queries resolved/closed
  • SAE reconciliation complete
  • External data reconciliation complete
  • Protocol deviations finalized
• Database lock (DBL) execution + documentation

Outputs

• DMP, CRF/eCRF, edit-check specs
• Validated EDC database + audit trail
• Clean, locked analysis-ready data extracts

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3) Data Analysis + SAP (extra detailed SAP)

A. Where SAP sits

• Protocol = what/why (high-level stats)
• SAP = exactly how you will analyze, pre-specified before database lock
• Programming specs (sometimes separate) = variable derivations + dataset build rules

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B. SAP: recommended structure (what to include)

1) Administrative & governance

• SAP version history, author/reviewer approvals, effective date
• Links to protocol version, amendments, and rationale for SAP updates
• Blinding status for statisticians (who is blinded, who is unblinded)

2) Study overview (tight summary)

• Design, arms, randomization, stratification factors, visit schedule
• Primary objective/endpoint (exact wording consistent with protocol)

3) Estimands (increasingly required)

For each primary (and key secondary) endpoint:

• Population (who)
• Treatment condition (what comparison)
• Variable (endpoint definition)
• Intercurrent events handling (e.g., rescue medication, treatment discontinuation)
• Summary measure (difference in means, HR, OR, etc.)

4) Analysis populations (define precisely)

• ITT / Full Analysis Set: all randomized, analyzed as assigned
• Per-Protocol (PP): exact criteria (adherence thresholds, major deviations)
• Safety: all who received ≥1 dose (as treated)
• Optional:
  • mITT (only if defensible + pre-specified; define clearly)
  • Pharmacokinetic set / biomarker set

5) Trial conduct rules used in analysis

• Definition and classification of protocol deviations
  • Major vs minor, who adjudicates, timing of finalization
• Handling of mis-randomization and mistaken inclusions

6) General statistical principles

• Significance level (alpha), two-sided vs one-sided
• Confidence intervals approach
• Multiplicity strategy:
  • Hierarchical testing / gatekeeping / Bonferroni / Hochberg, etc.
• Covariate adjustment principles (pre-specified covariates, stratification factors)
• Center effects handling (fixed vs random effects; pooling rules)

7) Data handling conventions

• Baseline definition rules (visit windows; last observation prior to first dose)
• Derived variables rules (change from baseline, time-to-event definitions)
• Outliers (detection + whether excluded—usually not; handled via sensitivity)
• Transformations (log transform rules)
• Concomitant medications coding rules
• Rescue medication rules (and how they impact estimands)

8) Missing data strategy (must be explicit)

• Missingness assumptions: MCAR/MAR/MNAR (what you assume and why)
• Primary approach by endpoint type:
  • Continuous repeated measures: MMRM often assumes MAR
  • Binary: multiple imputation / tipping point / non-responder imputation (if relevant)
  • Time-to-event: censoring rules (precise)
• Sensitivity analyses:
  • Worst-case / best-case
  • Pattern mixture models / delta-adjustment
  • Tipping point analyses

9) Primary endpoint analysis (very specific)

For the primary endpoint, specify:

• Statistical model (exact)
• Estimand alignment
• Covariates included (and justification)
• Hypothesis statement
• Effect estimate + CI reporting
• Diagnostics (model checks) and fallback methods if assumptions fail

Examples of the “detail level”:

• If continuous: ANCOVA vs MMRM, baseline adjustment, visit-by-treatment interaction
• If time-to-event: Cox model + proportional hazards checks; censoring; KM summaries
• If binary: logistic regression; risk difference estimation method; exact tests if sparse

10) Secondary endpoints analysis

• List each endpoint with:
  • Model
  • Multiplicity placement (hierarchy rank or adjusted p-value approach)
  • Timepoints and summaries

11) Subgroup analyses (pre-specify, don’t fish)

• Subgroups: sex, age bands, severity strata, biomarker status, region, etc.
• Method: interaction tests (treatment × subgroup)
• Forest plot conventions and interpretation cautions

12) Sensitivity analyses (must be pre-specified)

• PP analysis, as-treated analysis (usually supportive)
• Alternative missing data assumptions
• Alternative model forms (robust regression, nonparametric)
• If noncompliance is big: consider CACE as supportive (define how estimated)

13) Interim analyses (if applicable)

• Timing rules (information fraction, event count)
• Alpha spending function / boundaries
• Who is unblinded, and what reports are generated
• Operational firewall procedures

14) Safety analysis (often the biggest section)

• Exposure summaries (duration, dose intensity)
• TEAEs, SAEs, AESIs:
  • Coding dictionary version (MedDRA)
  • Treatment-emergent definition window
  • Summaries by SOC/PT, severity, relatedness
  • Risk differences, incidence rates (if time-at-risk differs)
• Labs/vitals/ECG:
  • Shift tables (baseline → worst on-treatment grade)
  • Clinically significant thresholds
• Deaths and discontinuations:
  • Narratives plan (who writes, template, QC)

15) Patient-reported outcomes / QoL (if present)

• Scoring rules, missing item handling, responder definitions
• Timepoints and multiplicity

16) Data standards + outputs

• Dataset standards (SDTM/ADaM if used)
• TLF shells (Tables, Listings, Figures) included or referenced
• Mock outputs + footnotes conventions

17) Quality control & reproducibility

• Double programming / independent validation plan
• Audit trail of code, datasets, outputs
• Final SAP sign-off procedure

SAP Deliverables

• Final signed SAP (pre-DBL)
• TLF shells (mock tables/figures)
• Programming specs / analysis dataset specs (often separate but linked)
• Topline outputs + final TLF package

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C. Execution of analysis (after DB lock)

• Data freeze → DBL → pull analysis datasets
• Run primary analysis exactly per SAP
• QC and discrepancy resolution
• Generate:
  • Topline summary (fast sponsor decision-making)
  • Full TLFs + narratives inputs
• Document any deviations from SAP (rare; justified and logged)

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4) Summary Report to Sponsor

A. Types of sponsor-facing reporting (typical sequence)

1. Topline / Executive summary
   1. Primary endpoint result, key safety signals, major deviations
   2. Go/no-go decision support
2. Clinical Study Report (CSR)
   1. Full regulatory-style report (often aligned with ICH E3 structure)
   2. Integrated efficacy + safety + trial conduct
3. Supporting packages
   1. TLF appendix
   2. Patient narratives (deaths, SAEs, discontinuations)
   3. Data definition lists, audit trail evidence
   4. Protocol deviations listing and impact discussion

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B. CSR workplan tasks

• CSR shell creation early (while trial ongoing)
• Populate sections after DBL:
  • Disposition, baseline, efficacy, safety, deviations
• Medical writing + statistical review cycles
• QC steps:
  • Table/figure cross-checks
  • Consistency checks (numbers match across text, tables, listings)
  • Traceability (protocol ↔ SAP ↔ CSR)
• Sponsor sign-off and finalization
• Optional: manuscript drafting, conference abstract, registry reporting

Outputs

• Topline report
• Final CSR + appendices
• Sponsor slide deck (board-level summary)

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✅ Key takeaways

• Protocol + sample size sets the scientific contract of the trial.
• Project setup + data management ensures data are clean, traceable, auditable.
• SAP is the “no-flex” analysis rulebook (pre-DBL), far more detailed than protocol.
• Sponsor report/CSR translates results into decision- and regulator-ready format.