A Bayesian approach for correcting exposure misclassification in meta-analysis

Adjusting for misclassification of an exposure in an individual participant data meta-analysis

A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate unbiased estimation of adjusted and unadjusted exposure-outcome associations and between-study heterogeneity in IPD-MA, where the extent and nature of exposure misclassification may vary across studies. We present Bayesian methods that allow misclassification of binary exposure variables to depend on study- and participant-level characteristics. In an example of the differential diagnosis of dengue using two variables, where the gold standard measurement for the exposure variable was unavailable for some studies which only measured a surrogate prone to misclassification, our methods yielded more accurate estimates than analyses naive with regard to misclassification or based on gold standard measurements alone. In a simulation study, the evaluated misclassification model yielded valid estimates of the exposure-outcome association, and was more accurate than analyses restricted to gold standard measurements. Our proposed framework can appropriately account for the presence of binary exposure misclassification in IPD-MA. It requires that some studies supply IPD for the surrogate and gold standard exposure, and allows misclassification to follow a random effects distribution across studies conditional on observed covariates (and outcome). The proposed methods are most beneficial when few large studies that measured the gold standard are available, and when misclassification is frequent.

Measurement error in meta-analysis (MEMA)-A Bayesian framework for continuous outcome data subject to non-differential measurement error

Ideally, a meta-analysis will summarize data from several unbiased studies. Here we look into the less than ideal situation in which contributing studies may be compromised by non-differential measurement error in the exposure variable. Specifically, we consider a meta-analysis for the association between a continuous outcome variable and one or more continuous exposure variables, where the associations may be quantified as regression coefficients of a linear regression model. A flexible Bayesian framework is developed which allows one to obtain appropriate point and interval estimates with varying degrees of prior knowledge about the magnitude of the measurement error. We also demonstrate how, if individual-participant data (IPD) are available, the Bayesian meta-analysis model can adjust for multiple participant-level covariates, these being measured with or without measurement error.

Misleading meta-analyses of observational studies may generate unjustified alarms: The case of medications for nausea and vomiting in pregnancy

OBJECTIVES

Because observational studies often use imperfect measurements, results are prone to misclassification errors. We used as a motivating example the possible teratogenic risks of antiemetic agents in pregnancy since a large observational study recently showed that first-trimester exposure to doxylamine-pyridoxine was associated with significantly increased risk of congenital malformations as a whole, as well as central nervous system defects, and previous observational studies did not show such associations. A meta-analysis on this issue was carried out with the aim to illustrate how differential exposure and outcome misclassifications may lead to uncertain conclusions.

METHODS

Medline, searched to October 2019 for full text papers in English. Summary Odds Ratios (ORs) with confidence intervals (CIs) were calculated using random-effect models. Probabilistic sensitivity analyses were performed for evaluating the extension of differential misclassification required to account for the exposure-outcome association.

RESULTS

Summary ORs were 1.02 (95 % CI, 0.92-1.15), 0.99 (0.82-1.19) and 1.25 (1.08-1.44) for overall congenital, cardiocirculatory, and central nervous system malformations respectively. By assuming exposure and outcome bias factor respectively of 0.95 (i.e., newborns with congenital defects had exposure specificity 5% lower than healthy newborns) and 1.12 (i.e., exposed newborns had outcome sensitivity 12 % higher than unexposed newborns), summary OR of central nervous system defects became 1.13 (95 % CI, 0.99-1.29) and 1.17 (95 % CI, 0.99-1.38).

CONCLUSION

Observational investigations and meta-analyses of observational studies need cautious interpretations. Their susceptibility to several, often sneaky, sources of bias should be carefully evaluated.