On estimating a constrained bivariate random effects model for meta-analysis of test accuracy studies.

ABSTRACT: Tailored meta-analysis uses setting-specific knowledge for the test positive rate and disease prevalence to constrain the possible values for a test's sensitivity and specificity. The constrained region is used to select those studies relevant to the setting for meta-analysis using an unconstrained bivariate random effects model (BRM). However, sometimes there may be no studies to aggregate, or the summary estimate may lie outside the plausible or "applicable" region. Potentially these shortcomings may be overcome by incorporating the constraints in the BRM to produce a constrained model. Using a penalised likelihood approach we developed an optimisation algorithm based on co-ordinate ascent and Newton-Raphson iteration to fit a constrained bivariate random effects model (CBRM) for meta-an

SUBMITTER: Baragilly M 

PROVIDER: S-EPMC8829734 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature