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Optimal Information Collection Policies in a Markov Decision Process Framework.

ABSTRACT: BACKGROUND:The cost-effectiveness and value of additional information about a health technology or program may change over time because of trends affecting patient cohorts and/or the intervention. Delaying information collection even for parameters that do not change over time may be optimal. METHODS:We present a stochastic dynamic programming approach to simultaneously identify the optimal intervention and information collection policies. We use our framework to evaluate birth cohort hepatitis C virus (HCV) screening. We focus on how the presence of a time-varying parameter (HCV prevalence) affects the optimal information collection policy for a parameter assumed constant across birth cohorts: liver fibrosis stage distribution for screen-detected diagnosis at age 50. RESULTS:We prove that it may be optimal to delay information collection until a time when the information more immediately affects decision making. For the example of HCV screening, given initial beliefs, the optimal policy (at 2010) was to continue screening and collect information about the distribution of liver fibrosis at screen-detected diagnosis in 12 years, increasing the expected incremental net monetary benefit (INMB) by $169.5 million compared to current guidelines. CONCLUSIONS:The option to delay information collection until the information is sufficiently likely to influence decisions can increase efficiency. A dynamic programming framework enables an assessment of the marginal value of information and determines the optimal policy, including when and how much information to collect.

SUBMITTER: Cipriano LE

PROVIDER: S-EPMC6690493 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Optimal Information Collection Policies in a Markov Decision Process Framework.

Cipriano Lauren E LE Goldhaber-Fiebert Jeremy D JD Liu Shan S Weber Thomas A TA

Medical decision making : an international journal of the Society for Medical Decision Making 20180904 7

<h4>Background</h4>The cost-effectiveness and value of additional information about a health technology or program may change over time because of trends affecting patient cohorts and/or the intervention. Delaying information collection even for parameters that do not change over time may be optimal.<h4>Methods</h4>We present a stochastic dynamic programming approach to simultaneously identify the optimal intervention and information collection policies. We use our framework to evaluate birth co ...[more]

PMID: 30179585

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Project description:Background & aimsUncertainties exist surrounding the timing of liver transplantation (LT) among patients with acute-on-chronic liver failure grade 3 (ACLF-3), regarding whether to accept a marginal quality donor organ to allow for earlier LT or wait for either an optimal organ offer or improvement in the number of organ failures, in order to increase post-LT survival.MethodsWe created a Markov decision process model to determine the optimal timing of LT among patients with ACLF-3 within 7 days of listing, to maximize overall 1-year survival probability.ResultsWe analyzed 6 groups of candidates with ACLF-3: patients age ≤60 or >60 years, patients with 3 organ failures alone or 4-6 organ failures, and hepatic or extrahepatic ACLF-3. Among all groups, LT yielded significantly greater overall survival probability vs. remaining on the waiting list for even 1 additional day (p <0.001), regardless of organ quality. Creation of 2-way sensitivity analyses, with variation in the probability of receiving an optimal organ and expected post-transplant mortality, indicated that overall survival is maximized by earlier LT, particularly among candidates >60 years old or with 4-6 organ failures. The probability of improvement from ACLF-3 to ACLF-2 does not influence these recommendations, as the likelihood of organ recovery was less than 10%.ConclusionDuring the first week after listing for patients with ACLF-3, earlier LT in general is favored over waiting for an optimal quality donor organ or for recovery of organ failures, with the understanding that the analysis is limited to consideration of only these 3 variables.Lay summaryIn the setting of grade 3 acute-on-chronic liver failure (ACLF-3), questions remain regarding the timing of transplantation in terms of whether to proceed with liver transplantation with a marginal donor organ or to wait for an optimal liver, and whether to transplant a patient with ACLF-3 or wait until improvement to ACLF-2. In this study, we used a Markov decision process model to demonstrate that earlier transplantation of patients listed with ACLF-3 maximizes overall survival, as opposed to waiting for an optimal donor organ or for improvement in the number of organ failures.

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Optimal Information Collection Policies in a Markov Decision Process Framework.

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Optimal Information Collection Policies in a Markov Decision Process Framework.

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