  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 estimates 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 misclassification is
exchangeable 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.
