An illness–death stochastic model in the analysis of longitudinal dementia data

Abstract

SUMMARYA significant source of missing data in longitudinal epidemiological studies on elderly individuals is death. Subjects in large scale community-based longitudinal dementia studies are usually evaluated for disease status in study waves, not under continuous surveillance as in traditional cohort studies. Therefore, for the deceased subjects, disease status prior to death cannot be ascertained. Statistical methods assuming deceased subjects to be missing at random may not be realistic in dementia studies and may lead to biased results. We propose a stochastic model approach to simultaneously estimate disease incidence and mortality rates. We set up a Markov chain model consisting of three states, non-diseased, diseased and dead, and estimate the transition hazard parameters using the maximum likelihood approach. Simulation results are presented indicating adequate performance of the proposed approach.

Effect of common neuropathologies on progression of late life cognitive impairment

“…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.…”

A comparison of non-homogeneous Markov regression models with application to Alzheimer's disease progression

“…Previous studies have demonstrated the extent of bias induced by ignoring the competing risk of death when estimating the rate of progression to MCI and AD [10]. Markov process models are also most appropriate to the observational nature of studies of AD in which cognitive status is typically assessed at periodic clinic visits, giving rise to interval censored data.…”

“…A simple method for accommodating temporal non-homogeneity is to stratify the population into age groups and estimate transition rates separately within groups. This method has been previously applied to studies of AD [26, 10]. Recently a new piecewise model for transition intensities for transition to AD was proposed in which piecewise intensities are modeled as explicit functions of age and other time-dependent covariates [24].…”

Risk factors for transitions from normal to mild cognitive impairment and dementia

“…6,7 Previous studies of risk factors associated with these conversions have been limited to the study of amnestic MCI and APOE alleles as a risk factor but have not addressed the transitional nature of this clinical syndrome or state. 8,9 Other more detailed studies have ignored the intervening MCI states and studied risk factors for transitions from the nondemented state to dementia with death as a competing risk 10,11 or studied the progression of dementia through various stages until death. [12][13][14] In this study we analyze the transient nature of a single domain amnestic MCI state and all other MCI states combined into one state, mixed MCI.…”