Evidence Synthesis Methods to Estimate Subgroup Effects for Distributional Cost-effectiveness Analysis of New Treatments

Abstract

Distributional cost-effectiveness analysis (DCEA) is an extension of conventional cost-effectiveness analysis to quantify health equity impacts. Although health disparities are recognized as an important concern, the typical analyses conducted to inform health technology assessment of a new intervention do not include a DCEA. One of the reasons brought forward is the relative sparseness of the available evidence for a new intervention. The objective of this paper is to review advanced evidence synthesis methods to estimate subgroup specific treatment effects relevant for a DCEA of new interventions. The paper will outline the evidence needs and gaps, present alternative evidence synthesis methods followed by an illustrative example, and conclude with some practical recommendations. Evidence challenges for estimating relative treatment effects are due to lack of inclusion of relevant subgroups in the randomized controlled trials (RCTs), lack of access to individual patient data, small subgroups resulting in uncertain effects, and reporting gaps. Evidence synthesis methods can help overcome evidence gaps by considering all relevant direct, indirect, and external evidence simultaneously. Methods of potential relevance include (network) meta-analysis with shrinkage estimation, conventional (network) meta-regression analysis, multi-level (network) meta-regression analysis, and generalized evidence synthesis. For a new intervention for which only RCT evidence is available and no real-world data, estimates can be improved if the assumption of exchangeable subgroup effects or the shared or exchangeable effect-modifier assumption among competing interventions can be defended. Future research is needed to assess the pros and cons of different methods for different data gap scenarios.