Adjusting for publication bias in meta-analysis via inverse probability weighting using clinical trial registries

TL;DR

This paper introduces a straightforward method to correct publication bias in meta-analyses by leveraging unpublished trial data from registries, using inverse probability weighting to improve estimate accuracy and confidence interval reliability.

Contribution

It develops a novel IPW-based approach utilizing clinical trial registry data to adjust for publication bias, simplifying bias correction compared to existing likelihood-based methods.

Findings

Estimators effectively eliminate publication bias.

Bootstrap confidence intervals outperform asymptotic ones.

Method provides reliable bias correction in simulations.

Abstract

Publication bias is a major concern in conducting systematic reviews and meta-analyses. Various sensitivity analysis or bias-correction methods have been developed based on selection models and they have some advantages over the widely used bias-correction method of the trim-and-fill method. However, likelihood methods based on selection models may have difficulty in obtaining precise estimates and reasonable confidence intervals or require a complicated sensitivity analysis process. In this paper, we develop a simple publication bias adjustment method utilizing information on conducted but still unpublished trials from clinical trial registries. We introduce an estimating equation for parameter estimation in the selection function by regarding the publication bias issue as a missing data problem under missing not at random. With the estimated selection function, we introduce the inverse probability weighting (IPW) method to estimate the overall mean across studies. Furthermore, the IPW versions of heterogeneity measures such as the between-study variance and the I2 measure are proposed. We propose methods to construct asymptotic confidence intervals and suggest intervals based on parametric bootstrapping as an alternative. Through numerical experiments, we observed that the estimators successfully eliminate biases and the confidence intervals had empirical coverage probabilities close to the nominal level. On the other hand, the asymptotic confidence interval is much wider in some scenarios than the bootstrap confidence interval. Therefore, the latter is recommended for practical use.