# A General Framework to Account for Outcome Reporting Bias in Systematic Reviews

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $336,645

## Abstract

Project Summary
 Comparative effectiveness research (CER) relies fundamentally on accurate and timely assessment of the
benefits and risks of different treatment options. Empirical evidence suggests that a median of 35% of efficacy
and 50% of safety outcomes per parallel group trials were incompletely reported, and statistically significant
outcomes had a higher likelihood of being fully reported compared to non-significant outcomes, both for
efficacy and safety. Such a bias is referred to as outcome reporting bias (ORB), i.e., “the selective reporting of
some outcomes but not others, depending on the nature and direction of the results (i.e., missing certain
outcomes).” Selective reporting can invalidate results from meta-analyses. As acknowledged in the Cochrane
handbook “Statistical methods to detect within-study selective reporting (i.e., outcome-reporting bias) are, as
yet, not well developed” (chapter 8.14.2, version 5.0.2), there is a critical need to develop methods specifically
accounting for ORB.
 In response to PA-16-160, the overall goal of this proposal is to develop, test and evaluate new statistical
methods and user-friendly software to account for ORB in multivariate and network meta-analyses. In this
proposal, we will focus on: (1) To propose and evaluate new methods for quantifying the evidence of ORB, to
adjusting for ORB, and to develop a procedure of sensitivity analysis under ORB in multivariate meta-analysis.
(2) To generalize the methods in Aim 1 to network meta-analyses (where more than 2 treatments are
compared simultaneously), and to propose methods to evaluate the evidence consistency. And (3) To develop
publicly available, user-friendly and well-documented software and apply the proposed methods to research
data sets. We will use carefully designed simulation studies to investigate the performance of the proposed
methods, apply the proposed methods to multiple existing databases, and develop statistical software for wider
research communities.
 We propose to perform empirical assessment of the strengths and weaknesses of these methods through
carefully designed simulation studies and, more importantly, applications to (network) meta-analyses of clinical
trials with multivariate outcomes. Completion of these three aims in this proposal will directly benefit the CER
program by providing state-of-the art methods implemented in user-friendly R package that will be made freely
available to the public. This has the potential to catalyze the development of many new methods, amplifying
the impact of our project.

## Key facts

- **NIH application ID:** 9999033
- **Project number:** 5R01LM012607-04
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Yong Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $336,645
- **Award type:** 5
- **Project period:** 2017-09-08 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999033

## Citation

> US National Institutes of Health, RePORTER application 9999033, A General Framework to Account for Outcome Reporting Bias in Systematic Reviews (5R01LM012607-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9999033. Licensed CC0.

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