Overview of methods for comparing the efficacies of drugs in the absence of head-to-head clinical trial data

Authors

  • Hansoo Kim,

    Corresponding author
    1. Melbourne EpiCentre, Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Parkville, VIC, Australia
    • Correspondence

      Mr Hansoo Kim MSc, Melbourne EpiCentre, Department of Medicine (RMH), The University of Melbourne, VIC 3010, Australia.

      Tel: +61 [0] 3 9342 8772

      Fax: +61 [0] 3 9342 8760

      E-mail: h.kim14@pgrad.unimelb.edu.au

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  • Lyle Gurrin,

    1. Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, Parkville, VIC, Australia
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  • Zanfina Ademi,

    1. Melbourne EpiCentre, Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Parkville, VIC, Australia
    2. Department of Epidemiology and Preventive Medicine, Monash University, Parkville, VIC, Australia
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  • Danny Liew

    1. Melbourne EpiCentre, Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Parkville, VIC, Australia
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Abstract

In most therapeutic areas, multiple drug options are increasingly becoming available, but there is often a lack of evidence from head-to-head clinical trials that allows for direct comparison of the efficacy and/or safety of one drug vs. another. This review provides an introduction to, and overview of, common methods used for comparing drugs in the absence of head-to-head clinical trial evidence. Naïve direct comparisons are in most instances inappropriate and should only be used for exploratory purposes and when no other options are possible. Adjusted indirect comparisons are currently the most commonly accepted method and use links through one or more common comparators. Mixed treatment comparisons (MTCs) use Bayesian statistical models to incorporate all available data for a drug, even data that are not relevant to the comparator drug. MTCs reduce uncertainty but have not yet been widely accepted by researchers, nor drug regulatory and reimbursement authorities. All indirect analyses are based on the same underlying assumption as meta-analyses, namely that the study populations in the trials being compared are similar.

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