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Exploring the research decision space: the expected value of information for sequential research designs
Susan Griffin1, Nicky J Welton, Karl Claxton
1Centre for Health Economics, University of York, York, United Kingdom. scg3@york.ac.uk
Summary
Sequential Expected Value of Partial Perfect Information (EVPPI) analysis expands research decision-making. This method assesses sequential research designs, offering new insights beyond single-parameter evaluations for greater research efficiency.
Area of Science:
- Decision Analysis
- Health Economics
- Research Methodology
Background:
- Expected value of information (EVI) analysis quantifies the expected gain from further research.
- Expected value of perfect information (EVPI) guides research by identifying key parameters.
- Traditional EVPPI focuses on single parameters, potentially missing sequential research benefits.
Purpose of the Study:
- To investigate the expected value of partial perfect information (EVPPI) and its application to research decisions.
- To examine the role of conditional and sequential EVPPI in EVI analysis.
- To broaden the scope of research decisions addressed by EVI.
Main Methods:
- Demonstrated calculation of EVPPI for single, grouped, conditional, and sequential parameters.
- Defined conditional EVPPI as information value dependent on prior information.
- Defined sequential EVPPI for multi-stage research designs.
Main Results:
- Conditional EVPPI differs from individual EVPPI.
- Sequential EVPPI incorporates joint and sequential parameter information.
- Sequential designs allow for research abandonment based on interim findings.
Conclusions:
- Incorporating sequential EVPPI enhances EVI analyses.
- Sequential EVPPI allows for the assessment of sequential research designs.
- This approach widens the research decision space in EVI.
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