Continuous-time multi-state stochastic processes are useful for modeling the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient cognitive states and death as a competing risk. Each subject's cognition is assessed periodically resulting in interval censoring for the cognitive states while death without dementia is not interval censored. Since back transitions among the transient states are possible, Markov chains are often applied to this type of panel data. In this manuscript, we apply a semi-Markov process in which we assume that the waiting times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and in which we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for likelihood estimation. We apply our model to a real dataset, the Nun Study, a cohort of 461 participants.
|Number of pages||16|
|Journal||Statistical Methods in Medical Research|
|State||Published - Dec 1 2016|
Bibliographical noteFunding Information:
This work was partially funded by the following grants to the University of Kentucky's Center on Aging (grant numbers R01 AG038651 and P30 AG028383) from the National Institute on Aging and a grant to the University of Kentucky's Center for Clinical and Translational Science (grant number U54 RR031263) from the National Center for Advancing Translational Science.
© SAGE Publications.
- back transitions
- interval censoring
- panel data
ASJC Scopus subject areas
- Statistics and Probability
- Health Information Management