Estimation of Multi-state Models with Missing Covariate Values Based on Observed Data Likelihood.

Continuous-time multi-state models are commonly used to study diseases with multiple stages. Potential risk factors associated with the disease are added to the transition intensities of the model as covariates, but missing covariate measurements arise frequently in practice. We propose a likelihood-based method that deals efficiently with a missing covariate in these models.

Multi-state models and missing covariate data: Expectation-Maximization algorithm for likelihood estimation.

Multi-state models have been widely used to analyze longitudinal event history data obtained in medical and epidemiological studies. The tools and methods developed recently in this area require completely observed data. However, missing data within variables of interest is very common in practice, and it has been an issue in applications.

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

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.

Self-reported memory complaints: implications from a longitudinal cohort with autopsies.

Objective: We assessed salience of subjective memory complaints (SMCs) by older individuals as a predictor of subsequent cognitive impairment while accounting for risk factors and eventual neuropathologies.

Methods: Subjects (n = 531) enrolled while cognitively intact at the University of Kentucky were asked annually if they perceived changes in memory since their last visit. A multistate model estimated when transition to impairment occurred while adjusting for intervening death.

Self-reported head injury and risk of late-life impairment and AD pathology in an AD center cohort.

Aims: To evaluate the relationship between self-reported head injury and cognitive impairment, dementia, mortality, and Alzheimer's disease (AD)-type pathological changes.

Methods: Clinical and neuropathological data from participants enrolled in a longitudinal study of aging and cognition (n = 649) were analyzed to assess the chronic effects of self-reported head injury.

Results: The effect of self-reported head injury on the clinical state depended on the age at assessment: for a 1-year increase in age, the OR for the transition to clinical mild cognitive impairment (MCI) at the next visit for participants with a history of head injury was 1.

Adjusting for mortality when identifying risk factors for transitions to mild cognitive impairment and dementia.

Risk factors for mild cognitive impairment (MCI) and dementia are often investigated without accounting for the competing risk of mortality, which can bias results and lead to spurious conclusions, particularly regarding protective factors. Here, we apply a semi-Markov modeling approach to 531 participants in the University of Kentucky Biologically Resilient Adults in Neurological Studies (BRAiNS) longitudinal cohort, over one-third of whom died without transitioning to a cognitively impaired clinical state. A semi-Markov approach enables a statistical study of clinical state transitions while accounting for the competing risk of death and facilitates insights into both the odds that a risk factor will affect clinical transitions as well as the age at which the transition to MCI or dementia will occur.

Mild cognitive impairment: statistical models of transition using longitudinal clinical data.

Mild cognitive impairment (MCI) refers to the clinical state between normal cognition and probable Alzheimer's disease (AD), but persons diagnosed with MCI may progress to non-AD forms of dementia, remain MCI until death, or recover to normal cognition. Risk factors for these various clinical changes, which we term "transitions," may provide targets for therapeutic interventions. Therefore, it is useful to develop new approaches to assess risk factors for these transitions.