Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal

作者：David Phillippo, A. E. Ades, Sofia Dias, Stephen Palmer, Keith R. Abrams, Nicky J. Welton · 发表于：Medical Decision Making · 年份：2017 · DOI：10.1177/0272989x17725740 · 被引用次数：413 · 研究领域：Health Systems, Economic Evaluations, Quality of Life、Pharmaceutical Economics and Policy、Healthcare cost, quality, practices

Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between the trials in the distribution of effect-modifying variables. Methods which relax this assumption are becoming increasingly common for submissions to reimbursement agencies, such as the National Institute for Health and Care Excellence (NICE). These methods use individual patient data from a subset of trials to form population-adjusted indirect comparisons between treatments, in a specific target population. Recently proposed population adjustment methods include the Matching-Adjusted Indirect Comparison (MAIC) and the Simulated Treatment Comparison (STC). Despite increasing popularity, MAIC and STC remain largely untested. Furthermore, there is a lack of clarity about exactly how and when they should be applied in practice, and even whether the results are relevant to the decision problem. There is therefore a real and present risk that the assumptions being made in one submission to a reimbursement agency are fundamentally different to-or even incompatible with-the assumptions being made in another for the same indication. We describe the assumptions required for population-adjusted indirect comparisons, and demonstrate how these may be used to generate comparisons in any given target population. We distinguish between anchored and unanchored comparisons according to whether a common comparator arm is used or not. Unanc...