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.
翻译:分析多种数据来源,包括个人参与者数据元分析(IPD-MA)的一个共同问题是二进制变量的分类错误。错误分类可能导致对模型参数的偏差估计,即使错误分类完全是随机的。我们的目标是制定统计方法,便利对经调整和未经调整的暴露-结果协会进行公正的估计,并便于在IPD-MA进行研究之间的异质性分析,其中暴露错误分类的程度和性质可能因不同研究而不同。我们介绍了巴伊西亚方法,这些方法允许对二进制暴露变量进行错误分类,以取决于研究和参与者级别的特征。在使用两种变量对登革热进行差异分析时,对不同类型进行差异分析时,即使对暴露变量的金质标准测量数据是完全随机的,但有些研究中只测量了隐蔽值的替代值,因此,在IDDA进行最常态化时,评估错误分类模型的计算结果比分析要准确得多。