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Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal

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Publication details

JournalMedical Decision Making
DateAccepted/In press - 8 Jun 2017
DateE-pub ahead of print - 19 Aug 2017
DatePublished (current) - 1 Feb 2018
Issue number2
Volume38
Number of pages12
Pages (from-to)200-211
Early online date19/08/17
Original languageUndefined/Unknown

Abstract

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 NICE. These 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. Unanchored comparisons make much stronger assumptions which are widely regarded as infeasible. We provide recommendations on how and when population adjustment methods should be used, and the supporting analyses that are required, in order to provide statistically valid, clinically meaningful, transparent and consistent results for the purposes of health technology appraisal. Simulation studies are needed to examine the properties of population adjustment methods and their robustness to breakdown of assumptions.

Bibliographical note

© The Authors, 2017

    Research areas

  • comparative effectiveness, indirect comparison, individual patient data, population adjustment

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