Trivariate generalized non linear mixed model with transformations for meta analysis of diagnostic accuracy

摘要

In this paper, a trivariate generalized non linear mixed model (TGLMM) using probit and complementary log-log transformations, is considered. These models are helpful in studying the complex relationship among the sensitivity (SN), specificity (SP) and disease prevalence (DP). For estimation of SN, SP, DP, positive (negative) predictive values (PPV and NPV) and positive (negative) likelihood ratios, Non-linear Mixed (NLMIXED) approach has been used. Model selection techniques are used to identify the best-fitting model for making statistical inference. The proposed trivariate non linear random effects models prove to be very useful in practice for meta-analysis of diagnostic accuracy studies.