Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample
نویسندگان
چکیده
منابع مشابه
Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample
In the Ades et al. method we calculate, for each sampled dataset, either the exact posterior expected net benefit for each decision option, or some close analytic approximation. We denote the analytically calculated posterior expected net benefit for data sample k and decision option d as NB
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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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ژورنال
عنوان ژورنال: Medical Decision Making
سال: 2015
ISSN: 0272-989X,1552-681X
DOI: 10.1177/0272989x15575286