Adaptive Experiments and a Rigorous Framework for Type I Error Verification and Computational Experiment Design

TL;DR

This thesis introduces innovative adaptive experiment designs, including clinical trials and bandit algorithms, with a focus on rigorous verification methods to ensure statistical validity and optimize decision-making in healthcare research.

Contribution

It presents new adaptive trial designs, advances in multi-armed bandit theory, and a rigorous simulation framework for verifying adaptive experiment properties.

Findings

Novel clinical trial designs for precision medicine

Enhanced bandit algorithms for healthcare applications

A rigorous framework for adaptive design verification

Abstract

This PhD thesis covers breakthroughs in several areas of adaptive experiment design: (i) (Chapter 2) Novel clinical trial designs and statistical methods in the era of precision medicine. (ii) (Chapter 3) Multi-armed bandit theory, with applications to learning healthcare systems and clinical trials. (iii) (Chapter 4) Bandit and covariate processes, with finite and non-denumerable set of arms. (iv) (Chapter 5) A rigorous framework for simulation-based verification of adaptive design properties.