Health Equity Impact Evaluation of New Treatments – Evidence Synthesis Methods to Overcome Data Gaps

Abstract

Abstract Background: Distributional cost-effectiveness analysis (DCEA) has been introduced as 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 formal health equity impact evaluation or DCEA. One of the reasons is that the clinical trials for new interventions frequently do not have the power or are not designed to estimate the required treatment effects for sub-populations across which you want to analyze equity. The objective of the paper is to discuss how gaps in evidence regarding equity-relevant subgroup effects for new and existing interventions can potentially be overcome with advanced Bayesian evidence synthesis methods to facilitate a credible model-based DCEA. Methods: First, the evidence needs and challenges for a model-based DCEA are outlined. Next, alternative evidence synthesis methods will be summarized, followed by an illustrative example of implementing these methods. The paper will conclude with some practical recommendations. Results: The key evidence challenges for a DCEA relate to estimating relative treatment effects due to lack of inclusion of relevant subgroups in the randomized controlled trials (RCTs), lack of access to individual patient data (IPD) for all trials, small subgroups resulting in uncertain effects, and reporting gaps. Advanced Bayesian evidence synthesis methods can help overcome evidence gaps by considering all relevant direct, indirect, and external evidence simultaneously. Methods discussed 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. Furthermore, formal expert elicitation is worthwhile to improve estimates. Conclusion: This paper provides an overview of advanced evidence synthesis methods that may help overcome typical gaps in the evidence base to perform model-based DCEA along with some practical recommendations. Future simulation studies are needed to assess the pros and cons of different methods for different data gap scenarios.