Methods, application, and risk of ‘adaptive’ health technology assessment

BACKGROUND: In striving for universal health coverage, many countries use evidence-based priority setting methods to allocate finite resources. To achieve allocative efficiency, cost-effectiveness analysis (CEA) is often central to these assessments. Current guidance and references cases for CEA aims to be rigorous, but achieving full rigor in practice can be challenging. ‘Adaptive’ health technology assessment (aHTA) methods have been proposed as a complement, that aim to systematically examine which elements of assessment methods should be simplified given time, data, and capacity constraints. While aHTA is used by various priority setting institutions and practitioners, no standardized approach exists. There is a need to clearly characterize and test aHTA methods to improve their standardization and enhance their replicability. METHODS: This thesis has three analytical sections, with the aim of designing and testing a standardized aHTA approach for CEA. The first defines and characterizes pre-existing aHTA methods. The second presents a novel approach to apply aHTA in a ‘real-world’ case study which assesses 49 cancers and develops potential cancer HBPs in Rwanda. The third conceptualizes and applies a method for evaluating the risk of conducting aHTA based on quantitative and qualitative uncertainties. RESULTS: This thesis presents a standardized aHTA approach for assessing the cost-effectiveness that captures all current worldwide approaches. Applying aHTA methods to the assessment of cancer in Rwanda helped to efficiently prioritize 49 cancers in a package focused on early-stage, curative care, within the data, time and capacity constraints. The innovative method designed for evaluating risk of conducting aHTA was tested in Rwanda and validated that the aHTA methods used in the cancer assessment were aligned with the available data and risk preferences of decision makers. CONCLUSION: In the absence of perfect data, aHTA may provide a feasible and useful tool for supporting evidence-based priority setting.