A nonstationary Markov transition model for computing the relative risk of dementia before death

Yu, Lei; Griffith, William S.; Tyas, Suzanne L.; Snowdon, David A.

doi:10.1002/sim.3828

Cited by 23 publications

(24 citation statements)

References 18 publications

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“…Markov models provide a popular statistical approach for capturing the progression of chronic diseases (Aalen, et al, 1997, Corpechot, et al, 2000, Deuffic-Burban, et al, 2002, Gentleman, et al, 1994, Hsieh, et al, 2002, Kay, 1986, Longini, et al, 1989, Rangel-Frausto, et al, 1998). The models are also widely applied to investigate neurodegenerative disease processes such as AD dementia (Abner, et al, 2012, Commenges, et al, 2004, Harezlak, et al, 2003, Kryscio, et al, 2006, Salazar, et al, 2007, Yu, et al, 2010). The central structure of the model is represented by a transition probability or intensity (i.e.…”

Section: Methodsmentioning

confidence: 99%

Effect of common neuropathologies on progression of late life cognitive impairment

Boyle

Leurgans

et al. 2015

Neurobiology of Aging

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Brain pathologies of Alzheimer’s, cerebrovascular and Lewy body diseases are common in old age, but the relationship of these pathologies with progression from normal cognitive function to the various stages of cognitive impairment is unknown. In this study, we fit latent Markov models from longitudinal cognitive data to empirically derive three latent stages corresponding to no impairment, mild impairment, and moderate impairment; then, we examined the associations of common neuropathologies with the rates of transition among these stages. Cognitive and neuropathological data were available from 653 autopsied participants in two ongoing cohort studies of aging who were cognitively healthy at baseline (mean baseline age 79.1 years) and had longitudinal cognitive data. On average, participants in these analyses developed mild impairment 5 years after enrollment, progressed to moderate impairment after an additional 3.4 years, and stayed impaired for 2.8 years until death. AD and chronic macroscopic infarcts were associated with a higher risk of progression to mild impairment and subsequently to moderate impairment. By contrast, Lewy bodies were associated only with progression from mild to moderate impairment. The 5-year probability of progression to mild or moderate impairment was 20% for persons without any of these three pathologies, 38% for AD only, 51% for AD and macroscopic infarcts, and 56% for AD, infarcts and Lewy bodies. Thus, the presence of AD pathology alone nearly doubles the risk of developing cognitive impairment in late life, and the presence of multiple pathologies further increases this risk over multiple years prior to death.

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Section: Methodsmentioning

confidence: 99%

Effect of common neuropathologies on progression of late life cognitive impairment

Boyle

Leurgans

et al. 2015

Neurobiology of Aging

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“…We employed a multistate model to examine the visit-to-visit transitions between the categories of parkinsonism or death (appendix e-1). 9,10 Table 2 summarizes the more than 15,000 transitions recorded for the participants included in these analyses. For each of the parkinsonism categories, participants were more likely to remain in that category at the next annual assessment than to transition to another category (e.g., 69.8% of those without parkinsonism at the previous examination remained without parkinsonism at their subsequent examination).…”

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confidence: 99%

Incident parkinsonism in older adults without Parkinson disease

Buchman

Leurgans

et al. 2016

Neurology

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Objective: To determine the incidence of parkinsonism in community-dwelling older adults without Parkinson disease.Methods: Four parkinsonian signs were assessed with a modified motor portion of the Unified Parkinson's Disease Rating Scale in 2,001 older adults without parkinsonism. We used Cox proportional hazards models to determine the associations of age and sex with incident parkinsonism (2 or more signs). We calculated the number of events per 1,000 person-years of observation in 3 age strata. Next, we investigated several potential risk factors for incident parkinsonism. Then, we examined longitudinal progression of parkinsonism using discrete-time multistate Markov models.Results: Average age at baseline was 76.8 years (SD 7.62 years). During an average of 5 years of follow-up, 964/2,001 (48.2%) developed parkinsonism. Age (hazard ratio [HR] 1.09, 95% confidence interval [CI] 1.08-1.10) but not male sex (HR 1.06, 95% CI 0.91-1.23) was associated with incident parkinsonism. The incidence of parkinsonism per 1,000 person-years of follow-up was 36.0 for adults ,75 years of age, 94.8 for those 75-84, and 160.5 for those 85 years or older. Depressive symptoms, neuroticism, urinary incontinence, sleep complaints, and chronic health conditions were associated with incident parkinsonism. Secondary analyses suggest that risk factors are linked with incident parkinsonism via early motor signs of parkinsonism and cognitive function. Transition modeling suggests that while parkinsonism may fluctuate, it is progressive in most older adults and its risk factors increase the odds of its progression. Motor impairments such as parkinsonian signs are common in older adults without Parkinson disease (PD) and are associated with a wide range of adverse health outcomes. Cross-sectional studies suggest that parkinsonian signs may affect up to half of older adults. Conclusions:1 Since parkinsonism is strongly associated with age, its incidence is likely to increase in our aging population and could constitute a major public health problem. Nonetheless, little data are available regarding age-and sex-specific incidence rates of parkinsonism.In this study, we employed a previously validated categorical measure of parkinsonism to determine the age-and sex-specific incidence of parkinsonism in 2,000 older communitydwelling older adults.2 Then we examined motor and cognitive measures that might capture early signs of parkinsonism and several possible risk factors to determine which were associated with incident parkinsonism.

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“…For example, reverse transitions are much easier to study under the discrete model as illustrated by Abner et al (2012). In addition, nonstationary risk factors such as a subject’s age can be incorporated into a Markov chain model (see, e.g., Yu et al (2010)). However, while Markov chain models can accommodate the simultaneous analysis of multiple events of interest and inclusion of competing risks through the states defined in the model, use of Markov chains have some potential limitations.…”

Section: Discussionmentioning

confidence: 99%

A comparison of time-homogeneous Markov chain and Markov process multi-state models

Wan

Lou

Abner

2016

Communications in Statistics: Case Studies, Data Analysis and A

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Time-homogeneous Markov models are widely used tools for analyzing longitudinal data about the progression of a chronic disease over time. There are advantages to modeling the true disease progression as a discrete time stationary Markov chain. However, one limitation of this method is its inability to handle uneven follow-up assessments or skipped visits. A continuous time version of a homogeneous Markov process multi-state model could be an alternative approach. In this article, we conduct comparisons of these two methods for unevenly spaced observations. Simulations compare the performance of the two methods and two applications illustrate the results.

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A nonstationary Markov transition model for computing the relative risk of dementia before death

Cited by 23 publications

References 18 publications

Effect of common neuropathologies on progression of late life cognitive impairment

Effect of common neuropathologies on progression of late life cognitive impairment

Incident parkinsonism in older adults without Parkinson disease

A comparison of time-homogeneous Markov chain and Markov process multi-state models

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