# Meta-analysis of Adverse Drug Effects in Clinical Trials

> **NIH NIH R21** · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · 2024 · $206,302

## Abstract

Project Summary/Abstract
Information on drug safety is critical to health care and policy decisions, as treatment recommendations hinge on
accurate knowledge of both efficacy and harms. However, assessments of drug safety from individual clinical trials are
often underpowered due to insufficient sample sizes and have limited generalizability due to restrictive inclusion/exclusion
criteria. Meta-analysis of clinical trials offers a unique opportunity to assess adverse event risks in a large sample size
for a broad population, but requires careful consideration of how to handle safety outcomes. Critically, methods that
are appropriate for assessing treatment benefits are inappropriate for assessing harms. A key distinction between safety
and efficacy data stems from the multifaceted nature of adverse events. There are many types of adverse events, each
correlated with different potential risk factors. The severity of these adverse events can vary widely, spanning from mild
to fatal. Moreover, many adverse drug effects are infrequent and typically suffer from incomplete reporting. In particular,
incomplete reporting of adverse events impacts the ability of systematic reviews to synthesize toxicity data, which can
promote a false impression of safety or misinform clinical and regulatory decisions.
To address these challenges, we propose to develop novel meta-analytic methods for combining safety data. Specifically,
our objectives are to develop meta-analysis approaches that can handle multivariate outcome data, account for the
severity of adverse events, and identify potential interactions among risk factors through the following specific aims:
Specific Aim 1: To develop meta-analysis methods for multivariate outcomes.
Specific Aim 2: To develop methods for meta-analysis with ordinal event grading.
Specific Aim 3: To develop a method to identify high-risk subgroups.
All methods developed will allow for incomplete reporting and be implemented as easy-to-use software. We will apply
the proposed methods to understand the drug toxicity profiles and elucidate risk factors of adverse events resulting from
cancer immunotherapy and BTK inhibitors. These findings can be used for risk stratification of patients and to inform
potential risk-reduction or monitoring strategies. More broadly, the products of this proposal will promote scientific
rigor in summary data integration, allowing appropriate inference on the safety of medical interventions and ultimately
enhancing patient care.
Our proposed research will establish a new modeling framework that will overcome the limitations of current meta-
analysis approaches to synthesize data and information on drug safety. This work directly addresses priorities in
the strategic plan of the National Library of Medicine to accelerate discovery and advance health through
data-driven research. The new approaches, together with publicly available software, will provide a useful tool for the
wider scientific community to co...

## Key facts

- **NIH application ID:** 10866721
- **Project number:** 1R21LM014534-01
- **Recipient organization:** PENNSYLVANIA STATE UNIV HERSHEY MED CTR
- **Principal Investigator:** Christine B Peterson
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $206,302
- **Award type:** 1
- **Project period:** 2024-06-07 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10866721

## Citation

> US National Institutes of Health, RePORTER application 10866721, Meta-analysis of Adverse Drug Effects in Clinical Trials (1R21LM014534-01). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10866721. Licensed CC0.

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