Matching-adjusted indirect comparison of efficacy outcomes in trials of calcipotriol plus betamethasone dipropionate foam and cream formulations for the treatment of plaque psoriasis

In the absence of head-to-head randomized trials, indirect comparisons of treatments across separate trials can be performed. However, these analyses may be biased by cross-trial differences in patient populations, sensitivity to modeling assumptions, and differences in the definitions of outcome measures. The objective of this study was to demonstrate how incorporating individual patient data (IPD) from trials of one treatment into indirect comparisons can address several limitations that arise in analyses based only on aggregate data. Matching-adjusted indirect comparisons (MAICs) use IPD from trials of one treatment to match baseline summary statistics reported from trials of another treatment. After matching, by using an approach similar to propensity score weighting, treatment outcomes are compared across balanced trial populations. This method is illustrated by reviewing published MAICs in different therapeutic areas. A novel analysis in attention deficit/hyperactivity disorder further demonstrates the applicability of the method. The strengths and limitations of MAICs are discussed in comparison to those of indirect comparisons that use only published aggregate data. Example applications were selected to illustrate how indirect comparisons based only on aggregate data can be limited by cross-trial differences in patient populations, differences in the definitions of outcome measures, and sensitivity to modeling assumptions. The use of IPD and MAIC is shown to address these limitations in the selected examples by reducing or removing the observed cross-trial differences. An important assumption of MAIC, as in any comparison of nonrandomized treatment groups, is that there are no unobserved cross-trial differences that could confound the comparison of outcomes. Indirect treatment comparisons can be limited by cross-trial differences. By combining IPD with published aggregate data, MAIC can reduce observed cross-trial differences and provide decision makers with timely comparative evidence. Copyright © 2012 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

The absence of head-to-head trials is a common challenge in comparative effectiveness research and health technology assessment. Indirect cross-trial treatment comparisons are possible, but can be biased by cross-trial differences in patient characteristics. Using only published aggregate data, adjustment for such biases may be impossible. Although individual patient data (IPD) would permit adjustment, they are rarely available for all trials. However, many researchers have the opportunity to access IPD for trials of one treatment, a new drug for example, but only aggregate data for trials of comparator treatments. We propose a method that leverages all available data in this setting by adjusting average patient characteristics in trials with IPD to match those reported for trials without IPD. Treatment outcomes, including continuous, categorical and censored time-to-event outcomes, can then be compared across balanced trial populations. The proposed method is illustrated by a comparison of adalimumab and etanercept for the treatment of psoriasis. IPD from trials of adalimumab versus placebo (n = 1025) were re-weighted to match the average baseline characteristics reported for a trial of etanercept versus placebo (n = 330). Re-weighting was based on the estimated propensity of enrolment in the adalimumab versus etanercept trials. Before matching, patients in the adalimumab trials had lower mean age, greater prevalence of psoriatic arthritis, less prior use of systemic treatment or phototherapy, and a smaller mean percentage of body surface area affected than patients in the etanercept trial. After matching, these and all other available baseline characteristics were well balanced across trials. Symptom improvements of ≥75% and ≥90% (as measured by the Psoriasis Area and Severity Index [PASI] score at week 12) were experienced by an additional 17.2% and 14.8% of adalimumab-treated patients compared with the matched etanercept-treated patients (respectively, both p < 0.001). Mean percentage PASI score improvements from baseline were also greater for adalimumab than for etanercept at weeks 4, 8 and 12 (all p < 0.05). Matching adjustment ensured that this indirect comparison was not biased by differences in mean baseline characteristics across trials, supporting the conclusion that adalimumab was associated with significantly greater symptom reduction than etanercept for the treatment of moderate to severe psoriasis.

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.