Regression-Based Approaches to Patient-Centered Cost-Effectiveness Analysis

Achieving comprehensive patient centricity in cost-effectiveness analyses (CEAs) requires a statistical approach that accounts for patients' preferences and clinical and demographic characteristics. Increased availability and accessibility of patient-level health-related utility data from clinical trials or observational database provide enhanced opportunities to conduct more patient-centered CEA. Regression-based approaches that incorporate patient-level data hold great promise for enhancing CEAs to be more patient centered; this paper provides guidance regarding two CEA approaches that apply regression-based approaches utilizing patient-level health-related utility and costs data. The first approach utilizes patient-reported preferences to determine patient-specific utility. This approach evaluates how individuals' unique clinical and demographic factors affect their utility and cost levels over the course of treatment. The underlying motivation of this approach is to produce CEA estimates that reflect patient-level utilities and costs while adjusting for socio-demographic and clinical factors to aid patient-centered coverage and treatment decision-making. In the second approach, patient utilities are estimated based on the clinically defined health states through which a patient may transition throughout the course of treatment. While this approach is grounded on the widely used Markov transition model, we refine the model to facilitate an enhancement in conducting regression-based analysis to achieve transparent understanding of differences in utilities and costs across diverse patient populations. We discuss the unique statistical challenges of each approach and describe how these analytical strategies are related to non-regression-based models in health services research.