Thesis Defense: Qiaohui Zhou

Candidate Name: Qiaohui Zhou, MS

Major: Biostatistics

Advisors: Ming Tan, PhD, & Ao Yuan, PhD

Title: “Estimand Framework in Clinical Research”

Location: W402, New Research Building

Abstract:
My thesis addresses key challenges in the design and analysis of clinical trials, focusing on baseline covariate imbalance, causal inference for studies with binary outcomes, and the relationship between intercurrent events (ICEs) and mediation analysis.

First, I propose a joint chi-square test for assessing baseline covariate imbalance, offering a more comprehensive evaluation than individual tests and potentially improving trial result validity.

Second, I develop an enhanced doubly robust estimator (eDRE) for binary outcomes in causal inference, using semiparametric models with nonparametric monotone link functions for propensity score and outcome models. This approach further enhances the robustness of traditional parametric doubly robust estimators. I present an iterative algorithm for parameter estimation and establish the estimator’s asymptotic properties. Simulations demonstrate the superior performance of my eDRE method compared to inverse probability score weighting and naive estimators across various scenarios.

Finally, I ascertain the connection between ICE handling strategies and mediation analysis within the estimand framework, clarifying distinctions and similarities between them. Causal diagrams are used to illustrate various strategies for handling ICEs, and then I develop statistical models for both ICE and mediation analyses to highlight their interconnections and differences. I perform simulation studies to illustrate the two types of post-trial events under various data generation settings, including scenarios combining ICE and mediator features.

My thesis work contributes more robust methods for assessing baseline imbalance, estimating causal effects with binary outcomes, and understanding ICE-mediation relationships. These contributions have implications for improving clinical trial design, analysis, and interpretation, enhancing medical research reproducibility. Future research directions include extending these methods to different types of trial designs and exploring real-world clinical applications. View dissertation defense draft.