Qijia He

I am a PhD candidate in the Department of Statistics at the University of Washington, where I am co-advised by professor Alex Luedtke and Bo Zhang. I am also very fortunate to collaborate with professor Yingqi Zhao, Ting Ye, and Fei Gao.

Previously, I received my MS degree in Statistics at the University of Washington in 2023. Before graduate school, I obtained my BS degree in Statistics from Sun Yat-sen University in 2021.

Contact me: heqj3@uw.edu

CV / Bio / LinkedIn

Selected Research

I have broad research interests in causal inference and machine learning. Specifically, I have been working on projects about individualized optimal decision-making, assessing variable importance for heterogeneous treatment effects, mediation analysis, and generalizability in causal inference.

Generalizing the Intention-to-Treat Effect of an Active Control from Historical Placebo-Controlled Trials: A Case Study of the Efficacy of Daily Oral TDF/FTC in the HPTN 084 Study
Qijia He, Fei Gao, Oliver Dukes, Sinead Delany-Moretlwe, Bo Zhang*
Journal of the American Statistical Association, 2024
code / supplement

We developed a formal potential outcomes framework for estimating the intention-to-treat (ITT) effect of an active control compared to the placebo in an active-controlled (AC) trial using data from historical placebo-controlled trials. Our proposal enables the estimation of ITT effect in AC trials where the placebo arm is lost due to ethical issues and considers the existence of unmeasured confounders by virtue of instrumental variables.

Estimating individualized treatment rules by optimizing the adjusted probability of a longer survival
Qijia He, Shixiao Zhang, Michael L LeBlanc, Yingqi Zhao*
Statistical Methods in Medical Research, 2024
code / supplement

We proposed a new criterion for constructing Individualized treatment rules that optimize the clinical benefit with survival outcomes, termed as the adjusted probability of a longer survival. This objective captures the likelihood of living longer with being on treatment, compared to the alternative, which provides an alternative and often straightforward interpretation to communicate with clinicians and patients. We developed a new method, optimal adjusted probability learning, to construct the optimal Individualized treatment rule by maximizing a nonparametric estimator of the adjusted probability of a longer survival for a decision rule.

Presentations

Generalizing the Intention-to-Treat Effect of an Active Control from Historical Placebo-Controlled Trials

- The Translational Data Science Integrated Research Center Retreat. Kirkland, WA, 2023.

- 20th Annual STI & HIV Research Symposium. Seattle, WA, 2023.

- American Causal Inference Conference. Seattle, WA, 2024

- Joint Statistical Meetings. Portland, OR, 2024.

Approximate Bayesian Computation (ABC)-Calibrated Microsimulation Model for Predicting HIV-1 Prevention Efficacy of Broadly Neutralizing Antibodies

- HVTN Africa Regional Meeting. Cape Town, South Africa, 2024.

Teaching Experience

	Department of Statistics, University of Washington Teaching Assistant in STAT 311 Elements of Statistical Methods (Winter 2024)
	Academic tutoring center, School of Mathematics, Sun Yat-sen University Tutor in Mathematical analysis (Fall 2018)
	TAL Education Group Teaching Assistant in primary-school Olympiad Mathematics (2017-2018)