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Quantile regression with a change-point model for longitudinal data: An application to the study of cognitive changes in preclinical alzheimer's disease.

ABSTRACT: Progressive and insidious cognitive decline that interferes with daily life is the defining characteristic of Alzheimer's disease (AD). Epidemiological studies have found that the pathological process of AD begins years before a clinical diagnosis is made and can be highly variable within a given population. Characterizing cognitive decline in the preclinical phase of AD is critical for the development of early intervention strategies when disease-modifying therapies may be most effective. In the last decade, there has been an increased interest in the application of change-point models to longitudinal cognitive outcomes prior to and after diagnosis. Most of the proposed statistical methodology for describing decline relies upon distributional assumptions that may not hold. In this article, we introduce a quantile regression with a change-point model for longitudinal data of cognitive function in persons bound to develop AD. A change-point in our model reflects the transition from the cognitive decline due to normal aging to the accelerated decline due to disease progression. Quantile regression avoids common distributional assumptions on cognitive outcomes and allows the covariate effects and the change-point to vary for different quantiles of the response. We provided an approach for estimating the model parameters, including the change-point, and presented inferential procedures based on the asymptotic properties of the estimators. A simulation study showed that the estimation and inferential procedures perform reasonably well in finite samples. The practical use of our model was illustrated by an application to longitudinal episodic memory outcomes from two cohort studies of aging and AD.

SUBMITTER: Li C

PROVIDER: S-EPMC4575281 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Quantile regression with a change-point model for longitudinal data: An application to the study of cognitive changes in preclinical alzheimer's disease.

Li Chenxi C Dowling N Maritza NM Chappell Rick R

Biometrics 20150417 3

Progressive and insidious cognitive decline that interferes with daily life is the defining characteristic of Alzheimer's disease (AD). Epidemiological studies have found that the pathological process of AD begins years before a clinical diagnosis is made and can be highly variable within a given population. Characterizing cognitive decline in the preclinical phase of AD is critical for the development of early intervention strategies when disease-modifying therapies may be most effective. In th ...[more]

PMID: 25892034

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Project description:Clinical trials testing treatments for Alzheimer disease (AD) are increasingly focused on cognitively normal individuals in the preclinical phase of the disease. To optimize observing a treatment effect, such trials need to enroll cognitively normal individuals likely to show cognitive decline over the duration of the trial.To identify which group of cognitively normal individuals shows the greatest cognitive decline over time based on their cerebrospinal fluid biomarker profile.In this cohort study, cognitively normal participants were classified into 1 of the following 4 hypothetical preclinical AD groups using baseline cerebrospinal fluid levels of A? and tau or A? and phosphorylated tau (p-tau): stage 0 (high A? and low tau), stage 1 (low A? and low tau), stage 2 (low A? and high tau), and suspected non-AD pathology (SNAP) (high A? and high tau). The data presented herein were collected between August 1995 and August 2014.An a priori cognitive composite score based on the following 4 tests previously shown to predict progression from normal cognition to symptom onset of mild cognitive impairment or dementia: Paired Associates immediate recall, Logical Memory delayed recall, Boston Naming, and Digit-Symbol Substitution. Linear mixed-effects models were used to compare the cognitive composite scores across the 4 groups over time, adjusting for baseline age, sex, education, and their interactions with time.Two hundred twenty-two cognitively normal participants were included in the analyses (mean follow-up, 11.0 years [range, 0-18.3 years] and mean baseline age, 56.9 years [range, 22.1-85.8 years]). Of these, 102 were stage 0, 46 were stage 1, 28 were stage 2, and 46 were SNAP. Individuals in stage 2 (low A? and high tau [or p-tau]) had lower baseline cognitive scores and a greater decline in the cognitive composite score relative to the other 3 groups (????-0.06 for all and P???.001 for the rate of decline for all). Individuals in stage 0, stage 1, and SNAP did not differ from one another in cognitive performance at baseline or over time (11.0 years) and showed practice-related improvement in performance. The APOE ?4 genotype was not associated with baseline cognitive composite score or the rate of change in the cognitive composite score.These results suggest that, to optimize observing a treatment effect, clinical trials enrolling cognitively normal individuals should selectively recruit participants with abnormal levels of both amyloid and tau (ie, stage 2) because this group would be expected to show the greatest cognitive decline over time if untreated.

Project description:Sleep monitoring may provide markers for future Alzheimer's disease; however, the relationship between sleep and cognitive function in preclinical and early symptomatic Alzheimer's disease is not well understood. Multiple studies have associated short and long sleep times with future cognitive impairment. Since sleep and the risk of Alzheimer's disease change with age, a greater understanding of how the relationship between sleep and cognition changes over time is needed. In this study, we hypothesized that longitudinal changes in cognitive function will have a non-linear relationship with total sleep time, time spent in non-REM and REM sleep, sleep efficiency and non-REM slow wave activity. To test this hypothesis, we monitored sleep-wake activity over 4-6 nights in 100 participants who underwent standardized cognitive testing longitudinally, APOE genotyping, and measurement of Alzheimer's disease biomarkers, total tau and amyloid-β42 in the CSF. To assess cognitive function, individuals completed a neuropsychological testing battery at each clinical visit that included the Free and Cued Selective Reminding test, the Logical Memory Delayed Recall assessment, the Digit Symbol Substitution test and the Mini-Mental State Examination. Performance on each of these four tests was Z-scored within the cohort and averaged to calculate a preclinical Alzheimer cognitive composite score. We estimated the effect of cross-sectional sleep parameters on longitudinal cognitive performance using generalized additive mixed effects models. Generalized additive models allow for non-parametric and non-linear model fitting and are simply generalized linear mixed effects models; however, the linear predictors are not constant values but rather a sum of spline fits. We found that longitudinal changes in cognitive function measured by the cognitive composite decreased at low and high values of total sleep time (P < 0.001), time in non-REM (P < 0.001) and REM sleep (P < 0.001), sleep efficiency (P < 0.01) and <1 Hz and 1-4.5 Hz non-REM slow wave activity (P < 0.001) even after adjusting for age, CSF total tau/amyloid-β42 ratio, APOE ε4 carrier status, years of education and sex. Cognitive function was stable over time within a middle range of total sleep time, time in non-REM and REM sleep and <1 Hz slow wave activity, suggesting that certain levels of sleep are important for maintaining cognitive function. Although longitudinal and interventional studies are needed, diagnosing and treating sleep disturbances to optimize sleep time and slow wave activity may have a stabilizing effect on cognition in preclinical or early symptomatic Alzheimer's disease.

Dataset Information

Quantile regression with a change-point model for longitudinal data: An application to the study of cognitive changes in preclinical alzheimer's disease.

Publications

Quantile regression with a change-point model for longitudinal data: An application to the study of cognitive changes in preclinical alzheimer's disease.

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