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Dimensions of Design Space: A Decision-Theoretic Approach to Optimal Research Design

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JournalMedical Decision Making
DatePublished - Nov 2009
Issue number6
Volume29
Number of pages18
Pages (from-to)643-660
Original languageEnglish

Abstract

Bayesian decision theory can be used not only to establish the optimal sample size and its allocation in a single clinical study but also to identify an optimal portfolio of research combining different types of study design. Within a single study, the highest societal payoff to proposed research is achieved when its sample sizes and allocation between available treatment options are chosen to maximize the expected net benefit of sampling (ENBS). Where a number of different types of study informing different parameters in the decision problem could be conducted, the simultaneous estimation of ENBS across all dimensions of the design space is required to identify the optimal sample sizes and allocations within such a research portfolio. This is illustrated through a simple example of a decision model of zanamivir for the treatment of influenza. The possible study designs include: 1) a single trial of all the parameters, 2) a clinical trial providing evidence only on clinical endpoints, 3) an epidemiological study of natural history of disease, and 4) a survey of quality of life. The possible combinations, samples sizes, and allocation between trial arms are evaluated over a range of cost-effectiveness thresholds. The computational challenges are addressed by implementing optimization algorithms to search the ENBS surface more efficiently over such large dimensions.

    Research areas

  • Bayesian decision theory, expected value of information, research design, cost-effectiveness analysis, HEALTH-CARE TECHNOLOGIES, EXPECTED VALUE, COST-EFFECTIVENESS, SAMPLE INFORMATION, CLINICAL-TRIAL, SENSITIVITY-ANALYSIS, RISK-MANAGEMENT, UNCERTAINTY, RATIOS

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