Heterogeneity in cost-effectiveness analysis: methods to explore the value of subgroups and individualized care in a collectively funded health system

Abstract: Decisions based on cost-effectiveness are usually made under imperfect and incomplete information. While the study of uncertainty has been mainly focused on the value of perfect information, the value of complete information has received less attention. This presentation is a summary of the work I have conducted for my doctoral thesis, which examines the value of understanding and characterizing heterogeneity for decision-making in healthcare.

The thesis proposes a methodological framework for a systematic cost-effectiveness subgroup analysis, providing guidance for the identification and selection of subgroups. It suggests that the value of heterogeneity should be examined by considering two dimensions: the value of making different decisions in different subgroups with current information (here termed static value); and the value of resolving parameter uncertainty conditional on a particular level of heterogeneity (here termed dynamic value). An empirical demonstration of such a framework is provided.

The study of heterogeneity for decision-making led to an examination of the policy agenda for individualization of care. This thesis also presents a conceptual framework to address two aspects of the implementation of individualized decisions. First, the implementation of unrestricted choices for treatment, which is based on the positive judgement about the expected health loss (or gain) associated with it; and second, the type of (individual) values that society seeks to maximise, which relates to a normative value judgement. The positive and normative elements of this framework are illustrated using a real case study.