# Use Bayesian methods to facilitate the data integration for complex clinical trials

> **NIH NIH R01** · INDIANA UNIVERSITY INDIANAPOLIS · 2024 · $320,247

## Abstract

Project Summary/Abstract
The primary goal of this research proposal is to develop general and efficient Bayesian statistical methods to
enhance drug discovery using complex clinical trial data. Rapid development in biomedical sciences is generat-
ing increasingly large and heterogeneous health-related data, including toxicity and efficacy endpoints, long-term
survival time, and surrogate biomarker profile. Although the data are heterogeneous by nature, they serve the
same central drug discovery question and multiple types of outcomes may be collected from the same individ-
ual. Therefore, a successful information integration of these “big data” generated during different periods of
complex clinical trials can improve the power of the hypothesis testing, speed the drug discovery process, and
enhance the individual ethics of the trials, among other benefits. However, significant efforts are needed to mit-
igate the gaps of the data generated from different platforms; otherwise, the accumulated inconsistencies and
biases may distort the statistical inference for complex clinical trials. We will tackle this important and challenging
research topic by developing a series of novel Bayesian statistical methods. In particular, we will (1) develop a
jointly modeling approach using the patient-derived organoids (PDO) and the paired clinical outcome to select
and verify personalized medicine (2) construct a Bayesian subgroup-specific dose optimization model to synthe-
size risk-benefit evidence across multi-dimensional heterogeneous data and (3) develop a Bayesian calibrated
network meta-analysis method to integrate the control information of master protocol trials during different ran-
domization stages. In addition, we will develop user-friendly web apps to facilitate the widespread application
of the proposed methods in clinical practice. All the aims in this proposal are driven by practical issues from
complex clinical trials. The proposed research are general and encompasses a variety of clinical trial settings,
including oncology and vaccine trials, phase I, II, and III trials, standard and master protocol trials, long-term
and short-term outcomes, and surrogate marker. The preliminary results show that the proposed methods can
substantially reduce the bias of the data and yield highly efficient and reliable performances, compared with other
existing methods.

## Key facts

- **NIH application ID:** 10917170
- **Project number:** 5R01GM150808-02
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Yong Zang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $320,247
- **Award type:** 5
- **Project period:** 2023-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10917170

## Citation

> US National Institutes of Health, RePORTER application 10917170, Use Bayesian methods to facilitate the data integration for complex clinical trials (5R01GM150808-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10917170. Licensed CC0.

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