# Improving the design and statistical analysis of cluster-randomized trials on tropical infectious diseases

> **NIH NIH R00** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $248,648

## Abstract

PROJECT SUMMARY
This Pathway to Independence Award application is submitted by a statistician committed to improving the
design and analysis of tropical infectious disease cluster-randomized trials (CRTs). Worldwide, hundreds of
CRTs are carried out annually to evaluate the effect of new interventions against infectious diseases,
especially in tropical developing countries experiencing dengue, Ebola, malaria, and other infectious disease
outbreaks. The scientific rigor of these CRTs relies on valid statistical analysis methods that adequately
address the complexity in the CRT designs. However, the emergence of CRTs with complex and novel designs
has outpaced the development of causal inference methods for data analysis. This gap represents a key
barrier to providing valid sample size calculation, efficient estimation, and correct interpretation of the
intervention effect estimates. The overarching goal of this research is to surmount this barrier by developing
valid, robust, and efficient statistical methods. Specifically, the applicant will address the statistical challenges
of three CRT designs: (1) covariate-adaptive randomization, which has been extensively used for reducing
baseline imbalance, (2) the test-negative design, which has been increasingly popular in recent years for
achieving cost-efficiency, and (3) the multi-arm stepped-wedge design, which has the potential to improve
flexibility and efficiency for future CRTs. In the K99 phase, the applicant will extend the empirical process
theory to handle covariate-adaptive randomization in CRTs and provide both theoretical and computation
evaluations of current statistical models. During the first year of the R00 phase, the applicant will focus on test-
negative designs in CRTs and eliminate the bias from differential healthcare-seeking behavior by
characterizing the underlying causal graph and performing inference on self-nondiagnosable symptoms.
Finally, the applicant will develop an optimal design that can simultaneously handle treatment roll-out, multiple
interventions, and various outcome types. The applicant will accomplish the research aims under the
mentorship of established researchers in infectious disease, statistics, and biostatistics to assure his transition
to a tenure-track faculty position in the R00 phase and his emergence as a leading infectious disease
biostatistician. At the University of Pennsylvania, the applicant enjoys rich internal resources of courses,
seminars, computational equipment, collaborations, and intellectual interactions with prestigious researchers;
furthermore, the applicant has access to external training opportunities including summer institutes, national
conferences, and hands-on learning in trial conduct in Kenya. These training activities will propel the research
career of the application, thereby supporting his achieving academic independence and ultimately leading a
research team to advance the research of infectious diseases.

## Key facts

- **NIH application ID:** 11075413
- **Project number:** 4R00AI173395-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Bingkai Wang
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $248,648
- **Award type:** 4N
- **Project period:** 2024-06-12 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11075413

## Citation

> US National Institutes of Health, RePORTER application 11075413, Improving the design and statistical analysis of cluster-randomized trials on tropical infectious diseases (4R00AI173395-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/11075413. Licensed CC0.

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