Population-adjusted treatment comparisons: Overview of approaches and recommendations from the NICE DSU

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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 trials in the distribution of effect-modifying variables. Several methods which relax this assumption, including Matching-Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC), are becoming increasingly common in industry-sponsored treatment comparisons where a company has access to individual patient data (IPD) from its own trials but only aggregate data from competitor trials. These methods use IPD to adjust for between-trial differences in covariate distributions. Another class of methods extend the standard network meta-regression framework to simultaneously incorporate evidence at the individual and aggregate level.
Drawing from a recent NICE Decision Support Unit Technical Support Document [1,2] we review the properties of population adjustment methods, and identify the key assumptions. Notably, there is a fundamental distinction between “anchored” and “unanchored” forms of indirect comparison, where a common comparator arm is or is not utilised to control for between-trial differences in prognostic variables, with the unanchored comparison making assumptions that are very hard to meet. Furthermore, both MAIC and STC as currently applied can only produce estimates that are valid for the populations in the competitor trials, which do not necessarily represent the decision population. We provide recommendations on how and when population adjustment methods should be used to provide statistically valid, clinically meaningful, transparent and consistent results for the purposes of health technology appraisal.

1. Phillippo, D.M., Ades, A.E., Dias, S., Palmer, S., Abrams, K.R., Welton, N.J. (2016) NICE DSU Technical Support Document 18: Methods for population-adjusted indirect comparisons in submission to NICE. Available from www.nicedsu.org.uk.
2. Phillippo, D.M., Ades, A.E., Dias, S., Palmer, S., Abrams, K.R., Welton, N.J. (2017) Methods for population-adjusted indirect comparisons in Health Technology Appraisal. Medical Decision Making, first published online August 19, 2017. DOI: 10.1177/0272989X17725740.
Original languageEnglish
Publication statusUnpublished - 27 Mar 2018
Event64th Biometrisches Kolloquium - Goethe University Frankfurt, Frankfurt, Germany
Duration: 26 Mar 201828 Mar 2018
http://www.biometrisches-kolloquium2018.de/index.php/en/

Conference

Conference64th Biometrisches Kolloquium
CountryGermany
CityFrankfurt
Period26/03/1828/03/18
Internet address

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