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Trial Design Advisory

Trial designs that withstand scrutiny — from review committees to regulators

I help biotech teams and academic investigators refine statistical designs before they trigger reviewer pushback.

The goal is not cleverness — it's credibility: designs that are defensible, interpretable, and aligned with real decision-making.

This is advisory work, not outsourced execution. I step in when teams need an experienced statistical perspective to pressure-test assumptions, clarify trade-offs, and anticipate reviewer concerns.

Services

Protocol Statistical Review

Identify design risks early — before they slow you down.

A focused review of your protocol's statistical core, including endpoints, estimands, sample size rationale, and interim strategy. I flag issues that commonly trigger reviewer pushback and suggest practical refinements.

Typical use cases:

  • Preparing for internal governance or scientific review
  • Strengthening grant or cooperative group submissions
  • Catching design inconsistencies before SAP finalization

Turnaround: typically 1–2 weeks

Sample Size & Power Consultation

Justify your design with defensible assumptions.

I help teams move beyond checkbox power calculations toward assumptions that are coherent, transparent, and aligned with the scientific question. This may include covariate adjustment strategies, alternative estimands, or simulation-based justification where appropriate.

Typical use cases:

  • Trials under pressure to "do more with less"
  • Reviewer skepticism around optimistic effect sizes
  • Designs where conventional formulas don't tell the full story

Adaptive Design Feasibility

Determine whether complexity actually pays off.

Adaptive features can improve efficiency — or quietly introduce risk. I evaluate whether adaptations meaningfully improve operating characteristics in your setting, and when simpler designs are the better choice.

Typical considerations:

  • Group sequential stopping for efficacy or futility
  • Bayesian or hybrid monitoring approaches
  • Response-adaptive or enrichment strategies

The output is a clear recommendation, not a default "yes."

Interim Analysis & Futility Planning

Make stopping rules interpretable, not performative.

I help design interim monitoring plans that support real decisions — balancing ethical considerations, statistical rigor, and operational feasibility.

Includes:

  • Predictive or conditional power–based futility
  • Alpha-spending approaches
  • Bayesian posterior decision rules with calibrated operating characteristics

Who this is for

This work is a good fit if you are:

  • Early-stage biotech teams (Series A/B) preparing pivotal or registrational studies with limited margin for redesign
  • Academic investigators developing fundable grant proposals or cooperative group protocols
  • CROs or research groups seeking independent statistical review
  • Medical device or diagnostics teams navigating complex endpoints with lean biometric resources

Typical outcomes include clearer protocols, stronger reviewer responses, and fewer late-stage statistical revisions.

What this is not

This is not hourly programming, full-service trial execution, or novelty for its own sake. If you're looking for hands-on implementation support, I'm happy to suggest alternatives.

Selected advisory experience

Examples of recent work include:

  • Statistical review of an adaptive oncology trial protocol
  • Sample size and futility strategy for an NIH R01 submission
  • Interim monitoring plan for a device feasibility study

Get in touch

If you're wrestling with a design decision — and want a second set of experienced eyes — feel free to reach out.

Email:

I typically respond within 48 hours.

If helpful, include:

  • Your project stage
  • The decision you're trying to make
  • Your timeline

No forms. No obligation.