Article type
Year
Abstract
Background
A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the presence of misclassification of binary variables. Misclassification may bias estimates of parameters (including covariate effects), even when the misclassification is entirely random. Available methods for addressing misclassification in the analysis of exposure-outcome associations do not account for between-study heterogeneity in IPD-MA.
Objective
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 or within studies.
Methods
We present Bayesian methods that allow misclassification of binary exposure variables to depend on study- and participant-level characteristics. We illustrate this in an example of the differential diagnosis of dengue using two variables, where the gold standard measurement for the exposure variable is unavailable for some studies which only measured a surrogate prone to misclassification. We present a simulation study to assess bias, root mean square error (RMSE), coverage and power in estimating an exposure-outcome association.
Results
In the example, our methods yielded estimates with less error than analyses naive with regard to misclassification or based on gold standard measurements alone. In our simulations, the evaluated misclassification model yielded valid estimates of the true exposure-outcome association, with less RMSE, greater power and similar coverage compared to an analysis restricted to available gold standard measurements.
Conclusions
Our proposed framework can appropriately account for the presence of binary exposure misclassification in IPD-MA. It requires that 1) some studies supply IPD for the surrogate and gold standard exposure and 2) misclassification is exchangeable across studies conditional on observed covariates (and outcome). Further work is needed to address other types of misclassification.
Patient or healthcare consumer involvement
Not applicable; we developed new statistical methods for researchers.
A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the presence of misclassification of binary variables. Misclassification may bias estimates of parameters (including covariate effects), even when the misclassification is entirely random. Available methods for addressing misclassification in the analysis of exposure-outcome associations do not account for between-study heterogeneity in IPD-MA.
Objective
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 or within studies.
Methods
We present Bayesian methods that allow misclassification of binary exposure variables to depend on study- and participant-level characteristics. We illustrate this in an example of the differential diagnosis of dengue using two variables, where the gold standard measurement for the exposure variable is unavailable for some studies which only measured a surrogate prone to misclassification. We present a simulation study to assess bias, root mean square error (RMSE), coverage and power in estimating an exposure-outcome association.
Results
In the example, our methods yielded estimates with less error than analyses naive with regard to misclassification or based on gold standard measurements alone. In our simulations, the evaluated misclassification model yielded valid estimates of the true exposure-outcome association, with less RMSE, greater power and similar coverage compared to an analysis restricted to available gold standard measurements.
Conclusions
Our proposed framework can appropriately account for the presence of binary exposure misclassification in IPD-MA. It requires that 1) some studies supply IPD for the surrogate and gold standard exposure and 2) misclassification is exchangeable across studies conditional on observed covariates (and outcome). Further work is needed to address other types of misclassification.
Patient or healthcare consumer involvement
Not applicable; we developed new statistical methods for researchers.