Quantitative Evidence Synthesis Methods for the Assessment of the Effectiveness of Treatment Sequences for Clinical and Economic Decision Making: A Review and Taxonomy of Simplifying Assumptions
Ruth A. Lewis (),
Alex J. Sutton and
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Ruth A. Lewis: North Wales Centre for Primary Care Research, College of Health and Behavioural Sciences, Bangor University
Dyfrig Hughes: Bangor University
Alex J. Sutton: University of Leicester
Clare Wilkinson: North Wales Centre for Primary Care Research, Bangor University
PharmacoEconomics, 2021, vol. 39, issue 1, No 3, 25-61
Abstract Sequential use of alternative treatments for chronic conditions represents a complex intervention pathway; previous treatment and patient characteristics affect both the choice and effectiveness of subsequent treatments. This paper critically explores the methods for quantitative evidence synthesis of the effectiveness of sequential treatment options within a health technology assessment (HTA) or similar process. It covers methods for developing summary estimates of clinical effectiveness or the clinical inputs for the cost-effectiveness assessment and can encompass any disease condition. A comprehensive review of current approaches is presented, which considers meta-analytic methods for assessing the clinical effectiveness of treatment sequences and decision-analytic modelling approaches used to evaluate the effectiveness of treatment sequences. Estimating the effectiveness of a sequence of treatments is not straightforward or trivial and is severely hampered by the limitations of the evidence base. Randomised controlled trials (RCTs) of sequences were often absent or very limited. In the absence of sufficient RCTs of whole sequences, there is no single best way to evaluate treatment sequences; however, some approaches could be re-used or adapted, sharing ideas across different disease conditions. Each has advantages and disadvantages, and is influenced by the evidence available, extent of treatment sequences (number of treatment lines or permutations), and complexity of the decision problem. Due to the scarcity of data, modelling studies applied simplifying assumptions to data on discrete treatments. A taxonomy for all possible assumptions was developed, providing a unique resource to aid the critique of existing decision-analytic models.
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