Abstract

Assessment of added value of stratified medicine (SM) interventions is complex and depends on many factors including the performance of the diagnostic test and the utility of the test for informing patient management. Modelling studies based on decision analysis is a well-recognized method used for health technology assessments (HTA’s) of health care interventions to analyse the consequences of decisions that are made under uncertainty. In this study, we address specific modelling issues associated with SM interventions and used depression to scrutinize the modelling approach with SM in particular. The model includes the stratification of patients into poor, normal and rapid metabolisers. The analysis outlines the importance of addressing input parameters such as test sensitivity and specificity and especially false negative and false positive considerations of the diagnostic test. This requires additional structural model complexity to establish the link between the test results and the consecutive treatment changes and outcomes and lead to a higher degree of uncertainty in economic models for SM compared with traditional ones. In case of depression, CYP450 testing will aid the decision maker (e.g. GP) in dose adjustment of antidepressant treatments, and in minimizing factors (adverse events interactions) that may influence patient’s compliance, thereby potentially reducing disease management costs.

Keywords: Depression, diagnostics, health economics, stratified medicine.