Evidence Synthesis for Decision Making 5: The Baseline Natural History Model. (July 2013)

• Record Type: Journal Article
• Authors: Dias, Sofia; Welton, Nicky J.; Sutton, Alex J.; Ades, A. E.
• Is Part Of: Medical decision making. Volume 33:Number 5(2013)
• Page Start: 657
• Page End: 670
• Publication Date: 2013-07
• Subjects: cost-effectiveness analysis -- Bayesian meta-analysis -- multiparameter evidence synthesis; Medical policy -- Periodicals; Clinical medicine -- Decision making -- Periodicals; Medicine -- Periodicals; Médecine clinique -- Prise de décision -- Périodiques; 362.1
• DOI: 10.1177/0272989X13485155
• Languages: English
• ISSNs: 0272-989X

Abstract

Most cost-effectiveness analyses consist of a baseline model that represents the absolute natural history under a standard treatment in a comparator set and a model for relative treatment effects. We review synthesis issues that arise on the construction of the baseline natural history model. We cover both the absolute response to treatment on the outcome measures on which comparative effectiveness is defined and the other elements of the natural history model, usually "downstream" of the shorter-term effects reported in trials. We recommend that the same framework be used to model the absolute effects of a "standard treatment" or placebo comparator as that used for synthesis of relative treatment effects and that the baseline model is constructed independently from the model for relative treatment effects, to ensure that the latter are not affected by assumptions made about the baseline. However, simultaneous modeling of baseline and treatment effects could have some advantages when evidence is very sparse or when other research or study designs give strong reasons for believing in a particular baseline model. The predictive distribution, rather than the fixed effect or random effects mean, should be used to represent the baseline to reflect the observed variation in baseline rates. Joint modeling of multiple baseline outcomes based on data from trials or combinations of trial and observational data is recommended where possible, as this is likely to make better use of available evidence, produce more robust results, and ensure that the model is internally coherent.