A hybrid model for combining case-control and cohort studies in systematic reviews of diagnostic tests

Abstract

Systematic reviews of diagnostic tests often involve a mixture of case-control and cohort studies. The standard methods for evaluating diagnostic accuracy focus only on sensitivity and specificity and ignore the information on disease prevalence that is contained in cohort studies. Consequently, such methods cannot provide estimates of measures related to disease prevalence, such as population-averaged or overall positive and negative predictive values, which reflect the clinical utility of a diagnostic test. We propose a hybrid approach that jointly models the disease prevalence along with diagnostic test sensitivity and specificity in cohort studies, and sensitivity and specificity in case-control studies. To overcome the potential computational difficulties in the standard full likelihood inference of the hybrid model proposed, we propose an alternative inference procedure based on composite likelihood. Such composite-likelihood-based inference does not suffer computational problems and maintains high relative efficiency. In addition, it is more robust to model misspecifications compared with standard full likelihood inference. We apply our approach to a review of the performance of contemporary diagnostic imaging modalities for detecting metastases in patients with melanoma.