PRM75 - Use Of Model Averaging Techniques in Cost-Effectiveness Analysis in Oncology

Abstract

Often in cost-effectiveness analysis (CEA) of oncologic drugs, survival data from a randomized controlled trial are extrapolated to a lifetime horizon using parametric regression techniques. To capture parameter uncertainty in the analysis, regression parameters along with other model parameters are varied in probabilistic sensitivity analysis. However, structural uncertainty in the choice of regression model is rarely investigated. This study discusses the use of model averaging and provides an example to address structural uncertainty in CEA. Using a cohort partition model, the numbers of patients in “progression-free”, “progressed”, and “dead” health states were calculated directly from progression-free survival (PFS) and overall survival (OS) curves. Weibull, exponential, lognormal, log-logistic, generalized gamma, and Gompertz parametric models were used to extrapolated these curves to a lifetime horizon. Total costs, life year (LY), and quality adjusted life year (QALY) for each regression model were estimated. Weighted results across all models were calculated, based on weights that were derived from Akaike’s or Bayesian Information Criterion (AIC or BIC) parameters. Evaluating solely on BIC values, the lognormal distribution was identified as the best model for both survival curves. This resulted in the lowest observed ICERs. When model selection was based on considerations involving the log-cumulative hazard plots, clinical plausibility, and AIC/ BIC for each distribution, the Weibull distribution was selected for both curves, resulting in a 29% and 27% increase in the ICER for QALY and LY, respectively. Similar increases were observed when model averaging was applied using BIC-derived weights. In this case, model averaging produced results that were similar to those where model selection was based on multiple criteria. Choice of parametric models often has the biggest impact on the outcomes in CEAs in oncology. Model averaging takes into account the structural uncertainty surrounding the choice of parametric models.