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• W2011750461 abstract "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." @default.
• W2011750461 created "2016-06-24" @default.
• W2011750461 creator A5077216572 @default.
• W2011750461 creator A5081688346 @default.
• W2011750461 date "2016-07-11" @default.
• W2011750461 modified "2023-09-24" @default.
• W2011750461 title "Semi-Markov models for interval censored transient cognitive states with back transitions and a competing risk" @default.
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• W2011750461 doi "https://doi.org/10.1177/0962280214534412" @default.
• W2011750461 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4370792" @default.
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