Integration of meta-analysis and economic decision modeling for evaluating diagnostic tests.

Meta-analysis of diagnostic test accuracy data is more difficult than of effectiveness data because of 1) statistical challenges of dealing with multiple measures of accuracy (e.g., sensitivity and specificity) simultaneously and 2) incorporating threshold effects. A number of meta-analysis models are in use, ranging from naïve synthesis of independent sensitivity and specificity to optimization of a hierarchical summary receiver operating characteristic (SROC) curve. Little work has been done on how such analyses should inform decision models. This article aims to present a unified framework for the synthesis of primary data and economic evaluation of alternative diagnostic testing strategies using Bayesian Markov Chain Monte Carlo simulation methods. The authors extend this previous work by using systematic review to derive model parameters, fully allowing for uncertainty in their estimation, and formally incorporating variability between study results into the decision analysis. Using a simple decision model comparing alternative testing strategies for suspected deep vein thrombosis as an example, the authors consider how to use outputs of different alternative meta-analysis models in decision models. They also explore the limitations of diagnostic test studies, particularly when there is no obvious threshold value. To correct some of the limitations of diagnostic test studies, they propose that tests with implicit and explicit thresholds should be studied using distinctly different frameworks. Specifically, when a threshold exists, quantitative threshold information should be included in meta-analysis models to aid interpretation of SROCs. Setting policy to relate to a specific point may be much more difficult for studies with implicit thresholds.