Biases in Individualized Cost-effectiveness Analysis: Influence of Choices in Modeling Short-Term, Trial-Based,...

The benefits and costs of a treatment are typically heterogeneous across individual patients. Randomized clinical trials permit the examination of individualized treatment benefits over the trial horizon but extrapolation to lifetime horizon usually involves combining trial-based individualized estimates of short-term risk reduction with less detailed (less granular) population life tables. However, the underlying assumption of equal post-trial life expectancy for low- and high-risk patients of the same sex and age is unrealistic. We aimed to study the influence of unequal granularity between models of short-term risk reduction and life expectancy on individualized estimates of cost-effectiveness of aggressive thrombolysis for patients with an acute myocardial infarction.

To estimate life years gained, we multiplied individualized estimates of short-term risk reduction either with less granular and with equally granular post-trial life expectancy estimates. Estimates of short-term risk reduction were obtained from GUSTO trial data (30,510 patients) using logistic regression analysis with treatment, sex, and age as predictor variables. Life expectancy estimates were derived from sex- and age-specific US life tables.

Based on sex- and age-specific, short-term risk reductions but average population life expectancy (less granularity), we found that aggressive thrombolysis was cost-effective (incremental cost-effectiveness ratio below $50,000) for women above age 49 y and men above age 53 y (92% and 69% of the population, respectively). Considering sex- and age-specific short-term mortality risk reduction and correspondingly sex- and age-specific life expectancy (equal granularity), aggressive thrombolysis was cost-effective for men above age 45 y and women above age 50 y (95% and 76% of the population, respectively).

Failure to model short-term risk reduction and life expectancy at an equal level of granularity may bias our estimates of individualized cost-effectiveness and misallocate resources.