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Population Adjustment Methods for Indirect Comparisons: A Review of National Institute for Health and Care Excellence Technology Appraisals

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)221-228
Number of pages8
JournalInternational Journal of Technology Assessment in Health Care
Issue number3
Early online date13 Jun 2019
DateSubmitted - 1 Feb 2019
DateAccepted/In press - 27 Apr 2019
DateE-pub ahead of print - 13 Jun 2019
DatePublished (current) - 26 Jun 2019


Objectives: Indirect comparisons via a common comparator (anchored comparisons) are commonly used in Health Technology Assessment. However, common comparators may not be available, or the comparison may be biased due to differences in effect modifiers between the included studies. Recently proposed population adjustment methods aim to adjust for differences between study populations in the situation where individual patient data are available from at least one study, but not all studies. They can also be used when there is no common comparator or for single-arm studies (unanchored comparisons). We aim to characterise the use of population adjustment methods in technology appraisals (TAs) submitted to the UK National Institute for Health and Care Excellence (NICE).
Methods: We reviewed NICE TAs published between 01/01/2010 and 20/04/2018.
Results: Population adjustment methods were used in 7% (18/268) of TAs. Most applications used unanchored comparisons (89%, 16/18), and were in oncology (83%, 15/18). Methods used included Matching-Adjusted Indirect Comparisons (89%, 16/18) and Simulated Treatment Comparisons (17%, 3/18). Covariates were included based on: availability, expert opinion, effective sample size, statistical significance, or cross validation. Larger treatment networks were commonplace (56%, 10/18), but current methods cannot account for this. Appraisal committees received results of population-adjusted analyses with caution and typically looked for greater cost effectiveness to minimise decision risk.
Conclusions: Population adjustment methods are becoming increasingly common in NICE TAs, though their impact on decisions has been limited to date. Further research is needed to improve upon current methods, and to investigate their properties in simulation studies.

    Research areas

  • network meta-analysis, technology assessment, effect modifier, bias, comparative effectiveness research, Comparative effectiveness research, Network meta-analysis, Effect modifier, Bias, Technology assessment

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