Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal

David Phillippo, Tony Ades, Sofia Dias, Stephen Palmer, Keith Abrams, Nicky Welton

Research output: Contribution to journalArticle (Academic Journal)

29 Citations (Scopus)
549 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)200-211
Number of pages12
JournalMedical Decision Making
Volume38
Issue number2
Early online date19 Aug 2017
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

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

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  • Projects

    Student Theses

    Calibration of Treatment Effects in Network Meta-Analysis using Individual Patient Data

    Author: Phillippo, D. M., 28 Nov 2019

    Supervisor: Welton, N. (Supervisor) & Dias, S. (Supervisor)

    Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)

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    Activities

    • 2 Participation in conference
    • 1 Invited talk
    • 1 Public talk, debate, discussion

    64th Biometrisches Kolloquium

    David M Phillippo (Invited speaker)

    27 Mar 2018

    Activity: Participating in or organising an event typesInvited talk

    CEN-ISBS Joint Conference on Biometrics & Biopharmaceutical Statistics

    David M Phillippo (Speaker)

    30 Aug 2017

    Activity: Participating in or organising an event typesParticipation in conference

    Population-adjusted treatment comparisons: assumptions, properties, and recommendations

    David M Phillippo (Speaker)

    4 Oct 2017

    Activity: Talk or presentation typesPublic talk, debate, discussion

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