Statistical Science

This section presents my Statistical Science support across study strategy, estimands, confirmatory inference, robustness, and modeling, focused on analysis planning, execution, and delivery readiness.

What this section demonstrates

• Scientific leadership in clinical development (beyond programming execution)
• Design-to-analysis continuity (objectives → estimands → SAP/TLF strategy → inference)
• Regulatory-grade rigor (multiplicity, missing data, sensitivity analyses, QC/validation)
• Applied modeling capability (e.g., MMRM for longitudinal endpoints, KM/Cox methods for time-to-event analyses, and PK/PD modeling foundations)
• Review-ready delivery mindset (traceability, reproducibility, and communication readiness)

How to navigate this section

You can read this section in order, or jump directly to the area most relevant to your review.

Scientific Positioning and Leadership

• Study Leadership and Scientific Contribution

Trial Design, Estimands, and Planning

• Study Design Overview
• Estimands and Intercurrent Events
• Sample Size and Power
• Randomization and Blinding
• SAP and TLF Shell Planning

Confirmatory Inference and Robustness

• Multiplicity Strategy
• Missing Data Strategy
• Sensitivity Analyses

Statistical Modeling

• Modeling Overview
• Modeling Methods and Applications
• MMRM Methods and Applications
• Survival Analysis Methods and Applications
• PK/PD Modeling Foundations

Quality, Validation, and Delivery Readiness

• QC, Validation, and Delivery Readiness

End-to-end workflow reflected in this portfolio

1. Define the scientific question
   • Clinical objectives, endpoints, estimand framing, and decision criteria
2. Design the trial and analysis framework
   • Design options, randomization/blinding, sample size, and SAP/TLF planning
3. Specify confirmatory inference and robustness
   • Multiplicity control, missing data strategy, and sensitivity analyses
4. Apply fit-for-purpose statistical models
   • Longitudinal, time-to-event, and PK/PD approaches aligned to study objectives
5. Deliver review-ready outputs with QC discipline
   • Validation checks, traceability, reproducibility, and communication readiness

What reviewers and hiring teams can expect

Note

Across this section, I emphasize: - Method selection rationale (why the approach fits the objective) - Assumption awareness (what must hold for conclusions to be reliable) - Interpretability (how results support clinical decision-making) - Execution readiness (how strategy translates into SAP/TLFs and production workflows)

Recommended reading paths

For scientific leadership and trial strategy

1. Study Leadership and Scientific Contribution
2. Study Design Overview
3. Estimands and Intercurrent Events

For confirmatory inference and statistical rigor

1. Multiplicity Strategy
2. Missing Data Strategy
3. Sensitivity Analyses

For applied modeling capability

1. Modeling Overview
2. MMRM Methods and Applications
3. Survival Analysis Methods and Applications
4. PK/PD Modeling Foundations

For delivery discipline and submission readiness

1. SAP and TLF Shell Planning
2. QC, Validation, and Delivery Readiness

Positioning statement

This section is designed to show how I approach statistical work as both a scientific and operational discipline: aligning study questions with estimands and analysis strategy, selecting appropriate methods, stress-testing conclusions, and delivering outputs with the quality expected in regulated environments.