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Exploring the Research Decision Space: The Expected Value of Information for Sequential Research Designs

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JournalMedical Decision Making
DatePublished - Mar 2010
Issue number2
Volume30
Number of pages8
Pages (from-to)155-162
Original languageEnglish

Abstract

Purpose. To investigate the expected value of partial perfect information (EVPPI) and the research decisions it can address. Methods. Expected value of information (EVI) analysis assesses the expected gain in net benefit from further research. Where the expected value of perfect information (EVPI) exceeds the costs of additional research, EVPPI can be used to identify parameters that contribute most to the EVPI and parameters with no EVPPI that may be disregarded as targets for further research. Recently, it was noted that parameters with low EVPPI for a one-off research design may be associated with high EVPPI when considered as part of a sequential design. This article examines the characteristics and role of conditional and sequential EVPPI in EVI analysis. Results. The calculation of EVPPI is demonstrated for single parameters, groups of parameters, and conditional and sequential EVPPI. Conditional EVPPI is the value of perfect information about one parameter, conditional on having obtained perfect information about another. Sequential EVPPI is the value of perfect information for a sequential research design to investigate first one parameter, then another. Conditional EVPPI differs from the individual EVPPI for a single parameter. Sequential EVPPI includes elements from the joint EVPPI for the parameters and the EVPPI for the first parameter in sequence. Sequential designs allow abandonment of research on the second parameter on the basis of additional information obtained on the first. Conclusions. The research decision space addressed by EVI analyses can be widened by incorporating sequential EVPPI to assess sequential research designs.

    Research areas

  • value of information, uncertainty, decision making, HEALTH-RISK-MANAGEMENT, PERFECT INFORMATION, UNCERTAINTY, FRAMEWORK

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