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A model-based correction for outcome reporting bias in meta-analysis.

John Copas¹, Kerry Dwan, Jamie Kirkham

¹Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.

Biostatistics (Oxford, England)

|November 12, 2013

Summary

Outcome reporting bias (ORB) in medical trials can significantly skew results. This study proposes a model to correct for ORB, showing that adjusted findings move towards the null, potentially overturning significance claims.

Keywords:

Bias correction Meta-analysis ORBIT classification Outcome reporting bias Selective reporting

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Area of Science:

Medical Research Methodology
Biostatistics
Evidence Synthesis

Background:

Selective outcome reporting is a known issue in medical trials.
This bias can lead systematic reviews to omit studies with non-significant results for certain outcomes.
Existing systematic reviews may overestimate treatment effects due to this bias.

Purpose of the Study:

To develop a statistical model for estimating the impact of outcome reporting bias (ORB) on meta-analysis results.
To quantify the effect of ORB on confidence intervals and p-values.
To provide a method for correcting ORB in systematic reviews.

Main Methods:

Utilized the methodology from the Outcome Reporting Bias (ORB) in Trials study.
Developed a likelihood-based statistical model to estimate bias.
Re-analyzed two contrasting examples of meta-analyses.

Main Results:

Correcting for ORB shifts estimated treatment effects towards the null hypothesis.
The bias can be substantial, potentially reversing conclusions of statistical significance.
A simple fixed-effects approximation was derived for practical estimation of ORB effects.

Conclusions:

Outcome reporting bias significantly impacts the reliability of meta-analyses.
Adjusting for ORB leads to more conservative and accurate assessments of treatment efficacy.
The proposed model and approximation offer tools to mitigate the effects of ORB in evidence synthesis.