Cognitive Reserve and Mild Cognitive Impairment

Background and Objectives Little is known about the effect of education or other indicators of cognitive reserve on the rate of reversion from mild cognitive impairment (MCI) to normal cognition (NC) or the relative rate (RR) of reversion from MCI to NC vs progression from MCI to dementia. Our objectives were to (1) estimate transition rates from MCI to NC and dementia and (2) determine the effect of age, APOE, and indicators of cognitive reserve on the RR of reversion vs progression using multistate Markov modeling. Methods We estimated instantaneous transition rates between NC, MCI, and dementia after accounting for transition to death across up to 12 assessments in the Nun Study, a cohort study of religious sisters aged 75+ years. We estimated RRs of reversion vs progression for age, APOE, and potential cognitive reserve indicators: education, academic performance (high school grades), and written language skills (idea density, grammatical complexity). Results Of the 619 participants, 472 were assessed with MCI during the study period. Of these 472, 143 (30.3%) experienced at least one reverse transition to NC, and 120 of the 143 (83.9%) never developed dementia (mean follow-up = 8.6 years). In models adjusted for age group and APOE, higher levels of education more than doubled the RR ratio of reversion vs progression. Novel cognitive reserve indicators were significantly associated with a higher adjusted RR of reversion vs progression (higher vs lower levels for English grades: RR ratio = 1.83; idea density: RR ratio = 3.93; and grammatical complexity: RR ratio = 5.78). Discussion Knowledge of frequent reversion from MCI to NC may alleviate concerns of inevitable cognitive decline in those with MCI. Identification of characteristics predicting the rate of reversion from MCI to NC vs progression from MCI to dementia may guide population-level interventions targeting these characteristics to prevent or postpone MCI and dementia. Research on cognitive trajectories would benefit from incorporating predictors of reverse transitions and competing events, such as death, into statistical modeling. These results may inform the design and interpretation of MCI clinical trials, given that a substantial proportion of participants may experience improvement without intervention

CONTINUOUS TIME MULTI-STATE MODELS FOR INTERVAL CENSORED DATA

Continuous-time multi-state models are widely used in modeling longitudinal data of disease processes with multiple transient states, yet the analysis is complex when subjects are observed periodically, resulting in interval censored data. Recently, most studies focused on modeling the true disease progression as a discrete time stationary Markov chain, and only a few studies have been carried out regarding non-homogenous multi-state models in the presence of interval-censored data. In this dissertation, several likelihood-based methodologies were proposed to deal with interval censored data in multi-state models. Firstly, a continuous time version of a homogenous Markov multi-state model with backward transitions was proposed to handle uneven follow-up assessments or skipped visits, resulting in the interval censored data. Simulations were used to compare the performance of the proposed model with the traditional discrete time stationary Markov chain under different types of observation schemes. We applied these two methods to the well-known Nun study, a longitudinal study of 672 participants aged ≥ 75 years at baseline and followed longitudinally with up to ten cognitive assessments per participant. Secondly, we constructed a non-homogenous Markov model for this type of panel data. The baseline intensity was assumed to be Weibull distributed to accommodate the non-homogenous property. The proportional hazards method was used to incorporate risk factors into the transition intensities. Simulation studies showed that the Weibull assumption does not affect the accuracy of the parameter estimates for the risk factors. We applied our model to data from the BRAiNS study, a longitudinal cohort of 531 subjects each cognitively intact at baseline. Last, we presented a parametric method of fitting semi-Markov models based on Weibull transition intensities with interval censored cognitive data with death as a competing risk. We relaxed the Markov assumption and took interval censoring into account by integrating out all possible unobserved transitions. The proposed model also allowed for incorporating time-dependent covariates. We provided a goodness-of-fit assessment for the proposed model by the means of prevalence counts. To illustrate the methods, we applied our model to the BRAiNS study

A MARKOV TRANSITION MODEL TO DEMENTIA WITH DEATH AS A COMPETING EVENT

The research on multi-state Markov transition model is motivated by the nature of the longitudinal data from the Nun Study (Snowdon, 1997), and similar information on the BRAiNS cohort (Salazar, 2004). Our goal is to develop a flexible methodology for handling the categorical longitudinal responses and competing risks time-to-event that characterizes the features of the data for research on dementia. To do so, we treat the survival from death as a continuous variable rather than defining death as a competing absorbing state to dementia. We assume that within each subject the survival component and the Markov process are linked by a shared latent random effect, and moreover, these two pieces are conditionally independent given the random effect and their corresponding predictor variables. The problem of the dependence among observations made on the same subject (repeated measurements) is addressed by assuming a first order Markovian dependence structure. A closed-form expression for the individual and thus overall conditional marginal likelihood function is derived, which we can evaluate numerically to produce the maximum likelihood estimates for the unknown parameters. This method can be implemented using standard statistical software such as SAS Proc Nlmixed©. We present the results of simulation studies designed to show how the model’s ability to accurately estimate the parameters can be affected by the distributional form of the survival term. Then we focus on addressing the problem by accommodating the residual life time of the subject’s confounding in the nonhomogeneous chain. The convergence status of the chain is examined and the formulation of the absorption statistics is derived. We propose using the Delta method to estimate the variance terms for construction of confidence intervals. The results are illustrated with applications to the Nun Study data in details

MULTI-STATE MODELS WITH MISSING COVARIATES

Multi-state models have been widely used to analyze longitudinal event history data obtained in medical studies. The tools and methods developed recently in this area require the complete observed datasets. While, in many applications measurements on certain components of the covariate vector are missing on some study subjects. In this dissertation, several likelihood-based methodologies were proposed to deal with datasets with different types of missing covariates efficiently when applying multi-state models. Firstly, a maximum observed data likelihood method was proposed when the data has a univariate missing pattern and the missing covariate is a categorical variable. The construction of the observed data likelihood function is based on the model of a joint distribution of the response longitudinal event history data and the discrete covariate with missing values. Secondly, we proposed a maximum simulated likelihood method to deal with the missing continuous covariate when applying multi-state models. The observed data likelihood function was approximated by using the Monte Carlo simulation method. At last, an EM algorithm was used to deal with multiple missing covariates when estimating the parameters of multi-state model. The EM algorithm would be able to handle multiple missing discrete covariates in general missing pattern efficiently. All the proposed methods are justified by simulation studies and applications to the datasets from the SMART project, a consortium of 11 different high-quality longitudinal studies of aging and cognition

PARAMETRIC ESTIMATION IN COMPETING RISKS AND MULTI-STATE MODELS

The typical research of Alzheimer\u27s disease includes a series of cognitive states. Multi-state models are often used to describe the history of disease evolvement. Competing risks models are a sub-category of multi-state models with one starting state and several absorbing states. Analyses for competing risks data in medical papers frequently assume independent risks and evaluate covariate effects on these events by modeling distinct proportional hazards regression models for each event. Jeong and Fine (2007) proposed a parametric proportional sub-distribution hazard (SH) model for cumulative incidence functions (CIF) without assumptions about the dependence among the risks. We modified their model to assure that the sum of the underlying CIFs never exceeds one, by assuming a proportional SH model for dementia only in the Nun study. To accommodate left censored data, we computed non-parametric MLE of CIF based on Expectation-Maximization algorithm. Our proposed parametric model was applied to the Nun Study to investigate the effect of genetics and education on the occurrence of dementia. After including left censored dementia subjects, the incidence rate of dementia becomes larger than that of death for age \u3c 90, education becomes significant factor for incidence of dementia and standard errors for estimates are smaller. Multi-state Markov model is often used to analyze the evolution of cognitive states by assuming time independent transition intensities. We consider both constant and duration time dependent transition intensities in BRAiNS data, leading to a mixture of Markov and semi-Markov processes. The joint probability of observing a sequence of same state until transition in a semi-Markov process was expressed as a product of the overall transition probability and survival probability, which were simultaneously modeled. Such modeling leads to different interpretations in BRAiNS study, i.e., family history, APOE4, and sex by head injury interaction are significant factors for transition intensities in traditional Markov model. While in our semi-Markov model, these factors are significant in predicting the overall transition probabilities, but none of these factors are significant for duration time distribution

Multistate Markov chains and their application to the Biologically Resilient Adults in Neurological Studies cohort

Dementia is increasingly recognized as a major and growing threat to public health worldwide, and there is a critical need for prevention and treatment strategies. However, it is necessary that appropriate methodologies are used in the identification of risk factors. The purpose of this dissertation research was to develop further the body of literature featuring Markov chains as an analytic tool for data derived from longitudinal studies of aging and dementia. Data drawn from 649 participants in the University of Kentucky’s Alzheimer’s Disease Center’s (UK ADC) Biologically Resilient Adults in Neurological Studies (BRAiNS) cohort, which was established in 1989 and follows adults age 60 years and older who are cognitively normal at baseline to death, were used to conduct three studies. The first study, “Mild cognitive impairment: Statistical models of transition using longitudinal clinical data,” shows that mild cognitive impairment is a stable clinical entity when a rigorous definition is applied. The second study, “Self-reported head injury and risk of cognitive impairment and Alzheimer’s-type pathology in a longitudinal study of aging and dementia,” shows that when the competing risk of death is properly accounted for, self-reported head injury is a clear risk factor for late-life dementia and is associated with increased beta-amyloid deposition in the brain. The third study, “Incorporating prior-state dependence among random effects and beta coefficients improves multistate Markov chain model fit,” shows that the effect of risk factors, like age, may not be constant over time and may be altered based on the subject’s cognitive state and that model fit is significantly improved when this is taken into account

MULTI-STATE MODELS FOR INTERVAL CENSORED DATA WITH COMPETING RISK

Multi-state models are often used to evaluate the effect of death as a competing event to the development of dementia in a longitudinal study of the cognitive status of elderly subjects. In this dissertation, both multi-state Markov model and semi-Markov model are used to characterize 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. Firstly, a multi-state Markov model with three transient states: intact cognition, mild cognitive impairment (M.C.I.) and global impairment (G.I.) and one absorbing state: dementia is used to model the cognitive panel data. A Weibull model and a Cox proportional hazards (Cox PH) model are used to fit the time to death based on age at entry and the APOE4 status. A shared random effect correlates this survival time with the transition model. Secondly, we further apply a Semi-Markov process in which we assume that the wait- ing times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and 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 the likelihood based estimation. At the end of this dissertation we extend a non-parametric “local EM algorithm” to obtain a smooth estimator of the cause-specific hazard function (CSH) in the presence of competing risk. All the proposed methods are justified by simulation studies and applications to the Nun Study data, a longitudinal study of late life cognition in a cohort of 461 subjects

Novel perspectives from existing data on early Alzheimer’s disease pathology and dementia care use

Alzheimer’s disease (AD) has a long disease duration and a progressive course. To stop or slow down cognitive decline as early as possible, intervention studies are increasingly focusing on the earliest stage of the disease. To evaluate the effectiveness of these interventions, one ideally would want to track patients from the earliest preclinical stage, where amyloid pathology exists but cognition is still intact, to the prodromal stage, where cognitive functioning is impaired, to later and increasingly severe stages of dementia. An alternative strategy is to re-use and combine data that were previously collected. Combining different data sources can improve generalizability of findings, efficiency of future clinical trials, and identification of persons best suited for treatment at different disease stages. The aim of this thesis was to examine relevant outcomes and endpoints related to amyloid pathology in pre-dementia stages, and to examine the disease trajectory and care duration after a dementia diagnosis. In this thesis, we used different data sources and data types ranging from biomarker data to registry data to examine relevant outcomes and endpoints in AD. The relevant outcomes and endpoints in this thesis are important for the monitoring of treatment effects and for personalized predictions of whether and how a patient might advance on the AD disease spectrum. Part I focuses on preclinical and prodromal stages of AD, and Part II focuses on the disease trajectory and duration of different types of care after a dementia diagnosis

TOPICS IN LOGISTIC REGRESSION ANALYSIS

Discrete-time Markov chains have been used to analyze the transition of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient states, and death as competing risk. A multinomial logistic regression model is used to estimate the probability distribution in each row of the one-step transition matrix that correspond to the transient states. We investigate some goodness of fit tests for a multinomial distribution with covariates to assess the fit of this model to the data. We propose a modified chi-square test statistic and a score test statistic for the multinomial assumption in each row of the transition probability matrix. Multinomial logistic regression with categorical covariates can be analyzed by contingency tables. Exact p-value of goodness of fit test can be calculated based on MCMC samples. We show a hybrid scheme of the sequential importance sampling (SIS) procedure and the MCMC procedure for two-way contingency tables. We apply the SIS-MCMC procedure to the Nun Study data, a cohort of 461 participants on aging disease. The presence of the APOE-4 allele, levels of education are included as covariates in the application. Different grouping methods on age are also discussed. Separating data into four groups based on quantiles of age is recommended in the Nun Study. The traditional logistic regression model restricts the analysis on observations with complete covariate data, and ignores the incomplete observations due to missing or censored covariates. However, much information is lost in this approach. We introduce a maximum likelihood estimation based on the joint distribution of binary response variable, complete covariate and a right censored covariate. Simulation results show that the estimates with the new method are more accurate than those with the traditional complete case method when the sample size is relatively small or medium, across different censoring pattern. The proposed method is also applied to a model to analyze the relationship between the presence of arteriolosclerosis and the stay time in mild cognitive impairment of patients from SMART Study

The course of disability in the very old :drivers and trajectories

PhD ThesisIn recent decades the rapid growth in the numbers of the very old, those aged 85 years and above, has made them the fastest growing age group of most populations worldwide. Nevertheless we know little about their health and disability, the latter being a particularly important aspect of quality of life for individuals but also more widely as a major determinant for residential care. This thesis uses a unique study, the Newcastle 85+ Study, a longitudinal, population based cohort study of people born in 1921 and aged 85 years at first interview in 2006, to explore the disablement process in very late life through three substantive sub-studies. In the first sub-study I explore how disability unfolds through the order of loss in basic and instrumental activities of daily living [(I)ADLs, these being the building blocks of disability. (I)ADLs were lost in a specific order, activities requiring long distance mobility and balance (for example shopping) being lost first and those requiring upper body strength (e.g. dressing, feeding) last and with little difference between men and women. The second sub-study examines the impact of specific diseases on disability onset and finds that arthritis, diabetes and cognitive impairment were similarly disabling for men and women, cardiac disease was more disabling in women, and cardiovascular disease and respiratory disease disabling for women only. The final sub-study uses novel statistical techniques to uncover patterns of disability from age 85 to 90. Four distinct trajectories of disability were found for both sexes, with a disability-free trajectory being identified in men but not women, and all other trajectories showing increasing levels of disability. These sub-studies are discussed in the light of other literature, the extent to which they explain the greater disability yet survival of women (the disability-survival paradox), and the implications for the future