Different evidence summaries have implications for contextualizing findings of meta-analysis of diagnostic tests.

To evaluate diagnostic tests, analysts use meta-analyses to provide inputs to parameters in decision models. Choosing parameter estimands from meta-analyses requires understanding the meta-analytic and decision-making contexts. We expand on an analysis comparing positron emission tomography (PET), PET with computed tomography (PET/CT), and conventional workup (CW) in women with suspected recurrent breast cancer. We discuss Bayesian meta-analytic summaries (posterior mean over a set of existing studies, posterior estimate in an existing study, posterior predictive mean in a new study) used to estimate diagnostic test parameters (prevalence, sensitivity, specificity) needed to calculate quality-adjusted life years in a decision model contextualizing PET, PET/CT, and CW. The mean and predictive mean give similar estimates, but the latter displays greater uncertainty. Namely, PET/CT outperforms CW on average but may not do better than CW when implemented in future settings. Selecting estimands for decision model parameters from meta-analyses requires understanding the relationship between decision settings and meta-analysis studies' settings, specifically whether the former resemble one or all study settings or represents new settings. We provide an algorithm recommending appropriate estimands as input parameters in decision models for diagnostic tests to obtain output parameters consistent with the decision context.

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