Variation in Variables that Predict Progression from MCI to AD Dementia over Duration of Follow-up

Li, Shanshan; Okonkwo, Ozioma C.; Albert, Marilyn; Wang, Mei‐Cheng

doi:10.7726/ajad.2013.1002

Cited by 27 publications

(42 citation statements)

References 46 publications

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“…This may explain why volumetric changes on MRI were reported to be better predictors than cognitive measures among cognitively normal individuals [32]. A similar point was also made in the literature [33,34]. Our analyses controlled for the presence of APOE and demographic variables that have been associated with cognitive decline or the likelihood of developing AD.…”

Section: Discussionmentioning

confidence: 62%

Prediction of Conversion to Alzheimer’s Disease with Longitudinal Measures and Time-To-Event Data

Chan

Doody

et al. 2017

JAD

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Background Identifying predictors of conversion to Alzheimer’s disease (AD) is critically important for AD prevention and targeted treatment. Objective To compare various clinical and biomarker trajectories for tracking progression and predicting conversion from amnestic mild cognitive impairment to probable AD. Methods Participants were from the ADNI-1 study. We assessed the ability of 33 longitudinal biomarkers to predict time to AD conversion, accounting for demographic and genetic factors. We used joint modelling of longitudinal and survival data to examine the association between changes of measures and disease progression. We also employed time-dependent receiver operating characteristic (ROC) method to assess the discriminating capability of the measures. Results 23 of 33 longitudinal clinical and imaging measures are significant predictors of AD conversion beyond demographic and genetic factors. The strong phenotypic and biological predictors are in the cognitive domain (ADAS-Cog; RAVLT), functional domain (FAQ) and neuroimaging domain (middle temporal gyrus and hippocampal volume). The strongest predictor is ADAS-Cog 13 with an increase of one SD in ADAS-Cog 13 increased the risk of AD conversion by 2.92 times. Conclusion Prediction of AD conversion can be improved by incorporating longitudinal change information, in addition to baseline characteristics. Cognitive measures are consistently significant and generally stronger predictors than imaging measures.

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Section: Discussionmentioning

confidence: 62%

Prediction of Conversion to Alzheimer’s Disease with Longitudinal Measures and Time-To-Event Data

Chan

Doody

et al. 2017

JAD

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“…Most of the current analysis was based on volumes of brain regions 24–26 . For example, AD patients and MCI patients have been shown to have 27% and 11% smaller hippocampal volumes, respectively, as compared with normal age-matched elderly 24 .…”

Section: A Motivating Clinical Studymentioning

confidence: 99%

Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease

Luo

2017

Stat Methods Med Res

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In the study of Alzheimer’s disease (AD), researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients’ disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model (FJM) to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. We then develop a Bayesian approach for parameter estimation and a dynamic prediction framework for predicting the subjects’ future outcome trajectories and risk of dementia, based on their scalar and functional measurements. The proposed Bayesian FJM provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. Our proposed model is evaluated by a simulation study and is applied to the motivating Alzheimer’s Disease Neuroimaging Initiative (ADNI) study.

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“…In [73], 148 MCI subjects were used to identify the most predictive neuropsychological measures. In [44], 139 MCI subjects in ADNI-1 were used to evaluate the predictive power of brain volume, ventricular volume, hippocampus volume, APOE status, cerebrospinal fluid (CSF) biomarkers, and behavioral scores. Their results show a moderately accurate prediction with the value of an area under the curve of 0.757 at 36 months, whereas they found that baseline volumetric MRI and behavioral scores were selectively predictive.…”

Section: Introductionmentioning

confidence: 99%

BFLCRM: A Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer’s disease

et al. 2015

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The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer’s disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM.

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Variation in Variables that Predict Progression from MCI to AD Dementia over Duration of Follow-up

Cited by 27 publications

References 46 publications

Prediction of Conversion to Alzheimer’s Disease with Longitudinal Measures and Time-To-Event Data

Prediction of Conversion to Alzheimer’s Disease with Longitudinal Measures and Time-To-Event Data

Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease

BFLCRM: A Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer’s disease

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