Expected value of perfect information: an empirical example of reducing decision uncertainty by conducting additional research

Abstract OBJECTIVE: Value of information (VOI) analysis informs decision-makers about the expected value of conducting more research to support a decision. This expected value of (partial) perfect information (EV(P)PI) can be estimated by simultaneously eliminating uncertainty on all (or some) parameters involved in model-based decision-making. This study aimed to calculate the EVPPI, before and after collecting additional information on the parameter of a probabilistic Markov model with the highest EVPPI. METHODS: The model assessed the 5-year costs per quality-adjusted life year (QALY) of three bronchodilators in chronic obstructive pulmonary disease (COPD). It had identified tiotropium as the bronchodilator with the highest expected net benefit. Total EVPI was estimated plus the EVPPIs for four groups of parameters: 1) transition probabilities between COPD severity stages; 2) exacerbation probabilities; 3) utility weights; and 4) costs. Partial EVPI analyses were performed using one-level and two-level sampling algorithms. RESULTS: Before additional research, the total EVPI was Euro 1985 per patient at a threshold value of Euro 20,000 per QALY. EVPPIs were Euro 1081 for utilities, Euro 724 for transition probabilities, and relatively small for exacerbation probabilities and costs. A large study was performed to obtain more precise EQ-5D utilities by COPD severity stages. After using posterior utilities, the EVPPI for utilities decreased to almost zero. The total EVPI for the updated model was reduced to Euro 1037. With an EVPPI of Euro 856, transition probabilities were now the single most important parameter contributing to the EVPI. CONCLUSIONS: This VOI analysis clearly identified parameters for which additional research is most worthwhile. After conducting additional research on the most important parameter, i.e., the utilities, total EVPI was substantially reduced.

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