Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal

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 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. 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 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.

Cost-Effectiveness of Alemtuzumab in the Treatment of Relapsing Forms of Multiple Sclerosis in the United States

“…The approach can be assumed valid because prior NMAs found only negligible treatment-effect modification owing to baseline patient characteristics, 21,30 whereas NICE guidance suggests that anchored indirect comparisons implicitly control for cross-study differences in prognostic characteristics. 31 Overall, the effect size and associated confidence intervals were similar to inputs used in the analysis conducted by the Institute for Clinical and Economic Review. 30 The base case for our analysis used the HR estimates for 6month confirmed disability worsening (CDW).…”

Comparative Efficacy of Daratumumab Monotherapy and Pomalidomide Plus Low-Dose Dexamethasone in the Treatment of Multiple Myeloma: A Matching Adjusted Indirect Comparison

“…The MAIC approach was also used in another study that assessed PFS and OS outcomes for different treatment regimens used in patients with relapsed and/or refractory MM who received ≥2 prior treatments . Recently, the National Institute for Health and Care Excellence published a technical support document on population‐adjusted indirect comparisons such as the MAIC method. While acknowledging their limitations, they recognize the value of such methods for Health Technology Assessment in the absence of a connected network of randomized evidence, or where single‐arm studies are involved.…”

“…In such situations, which are common for rare diseases, and in oncology trials for patients with poor prognosis, the approach of reweighting of individual patient data (IPD) can be used to adjust comparisons of absolute outcomes between trials for cross‐trial differences and should be preferred over naïve unadjusted comparisons . This approach was recently described by Phillippo et al as “unanchored” comparisons , in contrast to “anchored” comparisons of relative treatment effects between trials with a common treatment arm.…”

Network meta‐analysis of multicomponent interventions

“…It may happen that a network is disconnected, which means that the set of treatments is partitioned in two or more subsets such that there is no study that compares a treatment in one subset to any treatment in another subset. Some approaches to disconnected networks have been suggested (Béliveau, Goring, Platt, & Gustafson, ; Goring et al., ), some based on arm‐based NMA models (Hawkins, Scott, & Woods, ; Hong, Chu, Zhang, & Carlin, ), others based on methods for population‐ (or matching‐)adjusted indirect comparisons (Phillippo et al., ; Signorovitch et al., ; Veroniki, Straus, Soobiah, Elliott, & Tricco, ).…”