# Precision Medicine by Harmonizing Real World Evidence and RCT Data

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $395,611

## Abstract

Abstract
Combining information from multiple studies continues to be a cost-effective approach in
biomedical research. In traditional statistical literature, the associated analytic method is
coined as "meta analysis". However, the statistical tools for meta analysis were
developed under rather restricted settings. In developing the next generation meta
analysis methods, there are many new challenges ranging from increasing the
robustness of traditional meta analysis to enhancing the protection of data privacy in
sharing patient level information. In this proposal, we aim to address several important
analytic issues that arise from combining multiple studies. We expect that the planned
methodological development will be able to provide a general framework to effective
information pooling from various sources. We also aim to facilitate the development of
new regulatory pathways to integrate real world evidences into the drug development
process. The proposal contains three specific aims.
In Specific Aim 1, we plan to develop valid and general random effects meta-analysis
inferential procedures allowing the number of studies to be small or the study-specific
treatment effect estimator to be irregular, where the statistical inference based on
traditional random effects models fails. In Specific Aim 2, we plan to develop robust and
efficient procedures for estimating treatment effects by synthesizing information from
real world evidence data and randomized clinical trials. The broad patient population and
detailed patient information make large database such as electronic medical records a
valuable source for precision medicine research. Effectively extracting rich information
from real world evidence data has thus become a pressing need. In this aim, we propose
to develop an adaptive causal inferential procedure based on multiple studies to correct
biases from various sources under relaxed assumptions. In Specific Aim 3, we propose
to develop optimal estimation/prediction procedures based on data from multiple sources
in the presence of the data privacy concern and between study heterogeneity. The first
part of the aim is about a divide-and-conquer strategy bypassing the need of patient
level data to alleviate the privacy concern in data sharing. The second part of the aim is
about a set of statistical learning methods for predicting patients’ future outcome and
selecting the optimal treatment accounting for between study heterogeneities, when
patient level data can be shared.

## Key facts

- **NIH application ID:** 10098041
- **Project number:** 5R01HL089778-10
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** LU TIAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $395,611
- **Award type:** 5
- **Project period:** 2008-09-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10098041

## Citation

> US National Institutes of Health, RePORTER application 10098041, Precision Medicine by Harmonizing Real World Evidence and RCT Data (5R01HL089778-10). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10098041. Licensed CC0.

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