Covariate-adjusted estimators of diagnostic accuracy in randomized trials.

Abstract

Randomized controlled trials evaluating the diagnostic accuracy of a marker frequently collect information on baseline covariates in addition to information on the marker and the reference standard. However, standard estimators of sensitivity and specificity do not use data on baseline covariates and restrict the analysis to data from participants with a positive reference standard in the intervention arm being evaluated. Covariate-adjusted estimators for marginal treatment effects have been developed and been advocated for by regulatory agencies because they can improve power compared to unadjusted estimators. Despite this, similar covariate-adjusted estimators for marginal sensitivity and specificity have not yet been developed. In this manuscript, we address this gap by developing covariate-adjusted estimators for marginal sensitivity and specificity of a diagnostic test that leverage baseline covariate information. The estimators also use data from all participants, not just participants with a positive reference standard in the intervention arm being evaluated. We derive the asymptotic properties of the estimators and evaluate the finite sample properties of the estimators using simulations and by analyzing data on lung cancer screening.