Trial Design, Estimands, and Planning

TipWhat this section covers

This section highlights how I support early study statistical planning: study design and endpoint strategy, estimands, sample size/power, randomization/blinding, and SAP/TLF shell planning.

Overview

Strong statistical execution starts with strong planning. In this section, I show how I translate protocol intent into an analysis plan that teams can execute with confidence. The focus is on scientific clarity, fit-for-purpose design choices, early analysis readiness, and practical implementation in ADaM/TLF workflows.

  • Scientific clarity: align objectives, endpoints, and estimands to the clinical question
  • Design appropriateness: choose methods that fit the study purpose, phase, and operational constraints
  • Analysis readiness: define populations, assumptions, and analysis strategy early
  • Delivery feasibility: ensure plans translate into ADaM/TLF workflows and clear documentation

My goal is to help teams move from protocol intent to a practical, traceable, statistically sound analysis plan.

What you’ll find in this section

Study Design Overview

  • Trial design considerations across common development settings
  • Endpoint hierarchy and alignment to analysis strategy
  • How design choices shape analyses and interpretation

Estimands and Intercurrent Events

  • Define the treatment effect for the clinical question of interest
  • Address intercurrent events in a structured, transparent way
  • Align estimand strategy with the analysis approach and interpretation

Sample Size and Power

  • Translate objectives and effect assumptions into power-driven sample size planning
  • Assess sensitivity to variability, dropout, and key design assumptions
  • Document rationale clearly to support review and decision-making

Randomization and Blinding

  • Evaluate design implications for balance, bias control, and interpretability
  • Address operational and statistical considerations for implementation
  • Connect design choices to analysis populations, estimands, and reporting

SAP and TLF Shell Planning

  • Translate protocol intent and statistical strategy into executable SAP and shell plans
  • Define shells, populations, and analysis conventions early to support consistent implementation
  • Strengthen traceability and reduce rework during ADaM/TLF production

Representative planning contributions

  • Protocol/SAP alignment: clarify endpoint definitions, analysis populations, and planned methods
  • Estimand framing: maintain consistent language and decision logic for intercurrent events
  • Feasibility review: identify early risks in scope, timing, and data dependencies
  • Shell planning: structure TLF expectations to improve production efficiency and QC readiness

Working style

My planning approach is submission-minded and implementation-aware:

  • Start with the clinical question and estimand target
  • Make assumptions explicit and testable
  • Anticipate ADaM/TLF implementation implications
  • Document decisions clearly for cross-functional teams
  • Build plans that are rigorous and executable