A likelihood based sensitivity analysis for publication bias on summary ROC in meta-analysis of diagnostic test accuracy

TL;DR

This paper introduces a likelihood-based sensitivity analysis method to assess the impact of publication bias on summary ROC and AUC in meta-analyses of diagnostic tests, accounting for selective publication mechanisms.

Contribution

It extends Copas' likelihood-based sensitivity analysis to the bivariate model for diagnostic accuracy, enabling bias correction under various publication selection scenarios.

Findings

Method effectively evaluates publication bias impact on SROC and SAUC.

Simulation studies demonstrate the method's accuracy and robustness.

Application to real data illustrates practical utility.

Abstract

In meta-analysis of diagnostic test accuracy, summary receiver operating characteristic (SROC) is a recommended method to summarize the discriminant capacity of a diagnostic test in the presence of study-specific cutoff values and the area under the SROC (SAUC) gives the aggregate measure of test accuracy. SROC or SAUC can be estimated by bivariate modelling of pairs of sensitivity and specificity over the primary diagnostic studies. However, publication bias is a major threat to the validity of estimates in meta-analysis. To address this issue, we propose to adopt sensitivity analysis to make an objective inference for the impact of publication bias on SROC or SAUC. We extend Copas likelihood based sensitivity analysis to the bivariate normal model used for meta-analysis of diagnostic test accuracy to evaluate how much SROC or SAUC would change with different selection probabilities under several selective publication mechanisms dependent on sensitivity and/or specificity. The selection probability is modelled by a selection function on $t$-type statistic for the linear combination of logit-transformed sensitivity and specificity, allowing the selective publication of each study to be influenced by the cutoff-dependent $p$-value for sensitivity, specificity, or diagnostic odds ratio. By embedding the selection function into the bivariate normal model, the conditional likelihood is proposed and the bias-corrected SROC or SAUC can be estimated by maximizing the likelihood. We illustrate the proposed sensitivity analysis by reanalyzing a meta-analysis of test accuracy for intravascular device related infection. Simulation studies are conducted to investigate the performance of proposed methods.