Stratified medicine in schizophrenia: how accurate would a test of drug response need to be to achieve cost-effective improvements in quality of life?

Abstract

Objective: Stratified medicine refers to the use of tests that predict treatment response to drive treatment decisions for individual patient. The pharmacoeconomic implications of this approach in schizophrenia are unknown. We aimed to assess the cost-effectiveness of a hypothetical stratified medicine algorithm (SMA) compared with treatment as usual (TAU), for patients with schizophrenia who failed a first-line antipsychotic.

Methods: A decision analytic model with embedded Markov process was constructed, which simulated the health and cost outcomes for patients followed SMA or TAU over a lifetime horizon, from healthcare and social care perspective. In the base-case analysis, sensitivity and specificity of the stratifier were both set as 60%. Extensive sensitivity analyses were conducted to test the impact of uncertainty around the value of important parameters. The primary outcome was the incremental cost per quality-adjusted life year (QALY) gained.

Results: When both sensitivity and specificity of the stratified test were set at 60%, SMA appeared to be the optimal strategy as it produces more QALYs and incurs lower costs than TAU. This is robust to all scenarios tested. At a willingness-to-pay threshold of £20,000 per QALY, the probability for SMA to be the optimal strategy is 82.4%.

Conclusions: Our results suggest that use of any stratifier with a sensitivity and specificity over 60% is very likely to be cost-effective comparing to TAU, for psychotic patients who failed a first-line antipsychotic. This finding, however, should be interpreted with caution due to lack of evidence for clozapine as a second-line antipsychotic.