Estimating the number and length of episodes in disability using a Markov chain approach.

ABSTRACT:

Background

Markov models are a key tool for calculating expected time spent in a state, such as active life expectancy and disabled life expectancy. In reality, individuals often enter and exit states recurrently, but standard analytical approaches are not able to describe this dynamic. We develop an analytical matrix approach to calculating the expected number and length of episodes spent in a state.

Methods

The approach we propose is based on Markov chains with rewards. It allows us to identify the number of entries into a state and to calculate the average length of episodes as total time in a state divided by the number of entries. For sampling variance estimation, we employ the block bootstrap. Two case studies that are based on published literature illustrate how our m

SUBMITTER: Dudel C 

PROVIDER: S-EPMC7389377 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature