Med Decis Making - Strategies for efficient computation of the expected value of partial perfect information.

Tópicos

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Resumo

Expected value of information methods evaluate the potential health benefits that can be obtained from conducting new research to reduce uncertainty in the parameters of a cost-effectiveness analysis model, hence reducing decision uncertainty. Expected value of partial perfect information (EVPPI) provides an upper limit to the health gains that can be obtained from conducting a new study on a subset of parameters in the cost-effectiveness analysis and can therefore be used as a sensitivity analysis to identify parameters that most contribute to decision uncertainty and to help guide decisions around which types of study are of most value to prioritize for funding. A common general approach is to use nested Monte Carlo simulation to obtain an estimate of EVPPI. This approach is computationally intensive, can lead to significant sampling bias if an inadequate number of inner samples are obtained, and incorrect results can be obtained if correlations between parameters are not dealt with appropriately. In this article, we set out a range of methods for estimating EVPPI that avoid the need for nested simulation: reparameterization of the net benefit function, Taylor series approximations, and restricted cubic spline estimation of conditional expectations. For each method, we set out the generalized functional form that net benefit must take for the method to be valid. By specifying this functional form, our methods are able to focus on components of the model in which approximation is required, avoiding the complexities involved in developing statistical approximations for the model as a whole. Our methods also allow for any correlations that might exist between model parameters. We illustrate the methods using an example of fluid resuscitation in African children with severe malaria.

Resumo Limpo

expect valu inform method evalu potenti health benefit can obtain conduct new research reduc uncertainti paramet costeffect analysi model henc reduc decis uncertainti expect valu partial perfect inform evppi provid upper limit health gain can obtain conduct new studi subset paramet costeffect analysi can therefor use sensit analysi identifi paramet contribut decis uncertainti help guid decis around type studi valu priorit fund common general approach use nest mont carlo simul obtain estim evppi approach comput intens can lead signific sampl bias inadequ number inner sampl obtain incorrect result can obtain correl paramet dealt appropri articl set rang method estim evppi avoid need nest simul reparameter net benefit function taylor seri approxim restrict cubic spline estim condit expect method set general function form net benefit must take method valid specifi function form method abl focus compon model approxim requir avoid complex involv develop statist approxim model whole method also allow correl might exist model paramet illustr method use exampl fluid resuscit african children sever malaria

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