# Statistical Methods and Software for Multivariate Meta-analysis

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2022 · $324,967

## Abstract

Statistical Methods and Software for Multivariate Meta-analysis
Principal Investigator: Haitao Chu, M.D., Ph.D.
Summary
 Comparative effectiveness research (CER) aims to inform health care decisions concerning the benefits and
risks of different prevention strategies, diagnostic instruments and treatment options. A meta-analysis (MA) is a
statistical method that combines results of multiple independent studies to improve statistical power and to
reduce certain biases within individual studies. MA also has the capacity to contrast results from different studies
and identify patterns and sources of disagreement among those results. While many statistical methods for MA
have been proposed and investigated, important research gaps remain. The increasing number of prevention
strategies, assessment instruments and treatment options for a given disease condition, as well as the rapid
escalation in costs, have generated a need to simultaneously compare multiple options in clinical practice using
innovative and rigorous multivariate MA methods.
 Following the NIH strategic plan for data science and the National Library of Medicine priority area on
“integration of heterogeneous data types”, in response to PA-18-484, this proposal's overall goal is to develop
cutting-edge statistical methods to enhance the reproducibility, efficiency and generalizability of MA, as well as
to develop easy-to-use software. Specifically, in this proposal, we will: (1) examine the performance of skewness
of the standardized deviates for quantifying publication bias in univariate MA, and develop methods quantifying
publication bias in multivariate MA; (2) develop a Bayesian hierarchical summary receiver operating
characteristic (HSROC) network meta-analysis framework for simultaneously comparing multiple diagnostic
tests; (3) develop a causal inference framework accounting for post-randomization variables in multivariate MA;
and (4) develop open-source, cross-platform, publicly available and easy-to-use software (including R packages
and SAS macros) to implement the proposed MA methods.
 We will evaluate the strengths and weaknesses of these proposed methods versus existing MA methods
using many real case studies and extensive simulation studies. The proposed statistical methods will be broadly
applicable to meta-analysis. Completing these four aims will directly benefit the CER evidence base by providing
state-of-the-art methods implemented in user-friendly software including R packages and SAS macros, which
will be made freely available to the public. It will improve public health by facilitating prevention, diagnosis, and
treatment of cancers and cardiovascular, infectious, and other diseases.

## Key facts

- **NIH application ID:** 10405472
- **Project number:** 5R01LM012982-04
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Lifeng Lin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $324,967
- **Award type:** 5
- **Project period:** 2019-09-10 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10405472

## Citation

> US National Institutes of Health, RePORTER application 10405472, Statistical Methods and Software for Multivariate Meta-analysis (5R01LM012982-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10405472. Licensed CC0.

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