A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis

Patient-level health economic data collected alongside clinical trials are an important component of the process of technology appraisal. For end-of-life treatments, the modeling of cost-effectiveness data may involve some form of partitioned survival analysis, in which measures of quality of life and survival for pre- and postprogression periods are combined to generate aggregate measures of clinical benefits (e.g., quality-adjusted survival). In addition, resource use data are often collected and costs are calculated for each type of health service (e.g., treatment, hospital, or adverse events costs). A critical problem in these analyses is that effectiveness and cost data present some complexities, such as nonnormality, spikes, and missingness, which should be addressed using appropriate methods to avoid biased results. This article proposes a general Bayesian framework that takes into account the complexities of trial-based partitioned survival cost-utility data to provide more adequate evidence for policy makers. Our approach is motivated by, and applied to, a working example based on data from a trial assessing the cost-effectiveness of a new treatment for patients with advanced non–small-cell lung cancer. Highlights • This is the first article proposing a Bayesian modeling framework for patient-level partitioned survival cost-utility analysis that can jointly take into account the typical complexities of the data, such as correlation, skewness, and spikes at zeros. • The framework is defined using a modular structure that enables a flexible model specification in terms of a sequence of conditional parametric distributions that can be chosen based on the specific characteristics of each modeled variable (e.g., type of effectiveness or cost component). • The proposed methods overcome the limitations of standard approaches that ignore at least some of the complexities of the data and, although easier to implement and well established among practitioners, may mislead cost-effectiveness decisions of policy makers.