Study Design Overview

From protocol intent to analysis-ready design decisions

Overview

A strong study design connects scientific objectives to operational execution and statistical validity. I help translate protocol intent into clear, analysis-ready decisions and make assumptions explicit, so endpoints, populations, timing, and methods stay consistent from data capture through analysis and reporting.

Tip

Goal: align objectives, endpoints, estimands, and operational constraints early to reduce rework and support clear, defensible conclusions.

Core design elements I evaluate

Objectives and hypotheses

  • Confirm primary/secondary/exploratory objectives and how success will be measured.
  • Clarify the hypothesis framework (superiority, non-inferiority, equivalence) and the decision criteria.

Endpoints and estimands

  • Define endpoints precisely (what is measured, when it is assessed, and how it is derived).
  • Specify estimands aligned with ICH E9(R1):
    • Population
    • Treatment condition
    • Variable (endpoint)
    • Intercurrent event strategy (e.g., discontinuation, rescue medication, death)
    • Summary measure (e.g., difference in means, hazard ratio, ORR)

Study population and analysis sets

  • Define analysis populations and rules:
    • Intent-to-Treat (ITT)
    • Modified ITT (mITT) (if applicable, clearly justified)
    • Per-Protocol (PP)
    • Safety population
  • Ensure consistent rules across SAP, ADaM, and TLFs.

Visit structure and assessment schedule

  • Validate visit windows, baseline rules, and endpoint assessment timing.
  • Confirm alignment across the Schedule of Activities/visit schedule, CRF, and analysis requirements.

Randomization and stratification

  • Assess the randomization scheme and stratification factors for:
    • fit to the endpoint and planned analysis
    • operational feasibility and expected balance
  • Ensure stratification factors are captured cleanly and applied consistently in analysis.

Sample size and power assumptions

  • Review assumptions (effect size, variance/event rate, alpha, power, dropout).
  • Confirm planned analyses and multiplicity control align with the power strategy.
  • Identify sensitivity scenarios (e.g., higher dropout, lower event rate, delayed enrollment).

Multiplicity and decision rules

  • Define Type I error control across:
    • multiple endpoints
    • multiple timepoints
    • interim looks (if any)
    • subgroup claims (if any)
  • Confirm the hierarchy/adjustment approach is practical and clearly testable.

Missing data and intercurrent events

  • Anticipate missingness drivers and operational risks.
  • Align the primary strategy to the estimand and endpoint type:
    • Continuous endpoints: MMRM and/or MI, with tipping-point sensitivity analyses as needed
    • Time-to-event endpoints: censoring rules with pre-specified sensitivity analyses
  • Ensure the CRF supports capturing reasons for missingness and key intercurrent events.

Typical outputs from design alignment

  • Clear endpoint and estimand definitions ready for the SAP
  • Analysis population rules plus baseline and visit definitions
  • Key assumptions supporting sample size and multiplicity strategy
  • Data capture guidance for CRF design and SDTM mapping
  • Early risk register (data availability, timing, missing data, operational constraints)

How this connects to deliverables

  • Protocol → SAP: design decisions become analysis methods and decision rules
  • SAP → ADaM specs: derivations encode populations, baselines, visit windows, and endpoints
  • ADaM → TLFs: outputs follow shells, reflect estimands, and align with reporting standards