Using clinical trial registries to inform Copas selection model for publication bias in meta-analysis

TL;DR

This paper introduces a new method using clinical trial registry data to improve the Copas selection model for addressing publication bias in meta-analyses, resulting in more accurate parameter estimation.

Contribution

It proposes an alternative inference procedure that leverages registry information to estimate all parameters in the Copas model, enhancing bias detection accuracy.

Findings

Smaller biases in parameter estimates

More accurate confidence intervals

Effective re-analysis of published meta-analyses

Abstract

Prospective registration of study protocols in clinical trial registries is a useful way to minimize the risk of publication bias in meta-analysis, and several clinical trial registries are available nowadays. However, they are mainly used as a tool for searching studies and information submitted to the registries has not been utilized as efficiently as it could. In addressing publication bias in meta-analyses, sensitivity analysis with the Copas selection model is a more objective alternative to widely-used graphical methods such as the funnel-plot and the trim-and-fill method. Despite its ability to quantify the potential impact of publication bias, a drawback of the model is that some parameters not to be specified. This may result in some difficulty in interpreting the results of the sensitivity analysis. In this paper, we propose an alternative inference procedure for the Copas selection model by utilizing information from clinical trial registries. Our method provides a simple and accurate way to estimate all unknown parameters in the Copas selection model. A simulation study revealed that our proposed method resulted in smaller biases and more accurate confidence intervals than existing methods. Furthermore, two published meta-analyses had been re-analysed to demonstrate how to implement the proposed method in practice.