Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample
نویسندگان
چکیده
منابع مشابه
Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample
The partial expected value of perfect information (EVPI) quantifies the expected benefit of learning the values of uncertain parameters in a decision model. Partial EVPI is commonly estimated via a 2-level Monte Carlo procedure in which parameters of interest are sampled in an outer loop, and then conditional on these, the remaining parameters are sampled in an inner loop. This is computational...
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Health economic decision-analytic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value to the decision maker of reducing uncertainty through collecting new data. In the context of a particular decision problem, the value of a propos...
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Measures of decision sensitivity that have been applied to medical decision problems were examined. Traditional threshold proximity methods have recently been supplemented by probabilistic sensitivity analysis, and by entropy-based measures of sensitivity. The authors propose a fourth measure based upon the expected value of perfect information (EVPI), which they believe superior both methodolo...
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ژورنال
عنوان ژورنال: Medical Decision Making
سال: 2013
ISSN: 0272-989X,1552-681X
DOI: 10.1177/0272989x13505910