A Bayesian approach for correcting exposure misclassification in meta-analysis

Abstract

In observational studies, misclassification of exposure is ubiquitous and can substantially bias the estimated association between an outcome and an exposure. Although misclassification in a single observational study has been well studied, few papers have considered it in a meta-analysis. Meta-analyses of observational studies provide important evidence for health policy decisions, especially when large randomized controlled trials are unethical or unavailable. It is imperative to account properly for misclassification in a meta-analysis to obtain valid point and interval estimates. In this paper, we propose a novel Bayesian approach to filling this methodological gap. We simultaneously synthesize two (or more) meta-analyses, with one on the association between a misclassified exposure and an outcome (main studies), and the other on the association between the misclassified exposure and the true exposure (validation studies). We extend the current scope for using external validation data by relaxing the "transportability" assumption by means of random effects models. Our model accounts for heterogeneity between studies and can be extended to allow different studies to have different exposure measurements. The proposed model is evaluated through simulations and illustrated using real data from a meta-analysis of the effect of cigarette smoking on diabetic peripheral neuropathy.

Keywords: external validation data; meta-analysis; misclassification; observational studies.

FIGURE 1

Density plots and forest plots for MOV1+MOI3: the left panel is the posterior density plots for log odds ratio, sensitivity and specificity. The right panel is the forest plots. Circles represent study-specific posterior medians. In the forest plots of sensitivity and specificity, the solid horizontal lines denote the 95% credible intervals of the validation studies, while the dashed horizontal lines denote the 95% credible intervals of the main studies. The vertical dashed lines indicate the overall estimates.

FIGURE 2

Forest plot for the corrected and uncorrected log odds ratios. The solid lines denote 95% credible intervals of the corrected log odds ratios for the main studies. The dashed lines denote the 95% creidible intervals of the uncorrected log odds ratios for the main studies.

FIGURE 3

Power analysis: power to detect logOR ≠ 0