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.

Res

SUBMITTER: Cipriano LE 

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

REPOSITORIES: biostudies-literature