Med Decis Making - Estimating multiparameter partial expected value of perfect information from a probabilistic sensitivity analysis sample: a nonparametric regression approach.

Tópicos

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Resumo

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 computationally demanding and may be difficult if correlation between input parameters results in conditional distributions that are hard to sample from. We describe a novel nonparametric regression-based method for estimating partial EVPI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits). The method is applicable in a model of any complexity and with any specification of input parameter distribution. We describe the implementation of the method via 2 nonparametric regression modeling approaches, the Generalized Additive Model and the Gaussian process. We demonstrate in 2 case studies the superior efficiency of the regression method over the 2-level Monte Carlo method. R code is made available to implement the method.

Resumo Limpo

partial expect valu perfect inform evpi quantifi expect benefit learn valu uncertain paramet decis model partial evpi common estim via level mont carlo procedur paramet interest sampl outer loop condit remain paramet sampl inner loop comput demand may difficult correl input paramet result condit distribut hard sampl describ novel nonparametr regressionbas method estim partial evpi requir probabilist sensit analysi sampl ie set sampl drawn joint distribut paramet correspond net benefit method applic model complex specif input paramet distribut describ implement method via nonparametr regress model approach general addit model gaussian process demonstr case studi superior effici regress method level mont carlo method r code made avail implement method

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