Joint modeling of longitudinal drug using pattern and time to first relapse in cocaine dependence treatment data

Ye, Jun; Li, Yehua; Guan, Yongtao

doi:10.1214/15-aoas852

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…Yao (2007) proposed a shared latent process model. Yan et al (2017) proposed a shared random effect model with a two-step estimation, and Ye et al (2015) proposed a model for baseline longitudinal patterns and interval-censored event time data. To clarify, joint modeling here refers to the situation where the domain of function is longitudinal time.…”

Section: Introductionmentioning

confidence: 99%

Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease

Xiao

Luo

2021

Biometrics

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Studies of Alzheimer's disease (AD) often collect multiple longitudinal clinical outcomes, which are correlated and predictive of AD progression. It is of great scientific interest to investigate the association between the outcomes and time to AD onset. We model the multiple longitudinal outcomes as multivariate sparse functional data and propose a functional joint model linking multivariate functional data to event time data. In particular, we propose a multivariate functional mixed model to identify the shared progression pattern and outcome-specific progression patterns of the outcomes, which enables more interpretable modeling of associations between outcomes and AD onset. The proposed method is applied to the Alzheimer's Disease Neuroimaging Initiative study (ADNI) and the functional joint model sheds new light on inference of five longitudinal outcomes and their associations with AD onset. Simulation studies also confirm the validity of the proposed model. Data used in preparation of this article were obtained from the ADNI database.

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Section: Introductionmentioning

confidence: 99%

Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease

Xiao

Luo

2021

Biometrics

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“…They revealed that it is often the changing patterns of the longitudinal outcome, rather than the actual value at the moment of event, that affects a patient's survival risk. In the following studies, the model was further extended for partial follow‐up studies, interval‐censored time, and prediction . However, these models focus on a single longitudinal outcome and are not applicable to the studies of neurodegenerative diseases, such as AD, which collect multiple longitudinal measures with more complexity.…”

Section: Introductionmentioning

confidence: 99%

Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time‐to‐event data

Luo

2019

Statistics in Medicine

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This paper is motivated by combining serial neurocognitive assessments and other clinical variables for monitoring the progression of Alzheimer's disease (AD). We propose a novel framework for the use of multiple longitudinal neurocognitive markers to predict the progression of AD. The conventional joint modeling longitudinal and survival data approach is not applicable when there is a large number of longitudinal outcomes. We introduce various approaches based on functional principal component for dimension reduction and feature extraction from multiple longitudinal outcomes. We use these features to extrapolate the health outcome trajectories and use scores on these features as predictors in a Cox proportional hazards model to conduct predictions over time. We propose a personalized dynamic prediction framework that can be updated as new observations collected to reflect the patient's latest prognosis, and thus intervention could be initiated in a timely manner. Simulation studies and application to the Alzheimer's Disease Neuroimaging Initiative dataset demonstrate the robustness of the method for the prediction of future health outcomes and risks of target events under various scenarios.

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“…Li et al [10] propose a bivariate functional model mixed with a skewed continuous variable and a binary measurement. Ye et al [11] discuss a jointly modeling framework for a functional data response and a time-to-event outcomes. Tidemann-Miller et al [12] propose a multivariate mixed-response functional data model.…”

Section: Introductionmentioning

confidence: 99%

Three‐part joint modeling methods for complex functional data mixed with zero‐and‐one–inflated proportions and zero‐inflated continuous outcomes with skewness

Staudenmayer

Wang

Keadle

et al. 2017

Statistics in Medicine

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We take a functional data approach to longitudinal studies with complex bivariate outcomes. This work is motivated by data from a physical activity study that measured 2 responses over time in 5-minute intervals. One response is the proportion of time active in each interval, a continuous proportions with excess zeros and ones. The other response, energy expenditure rate in the interval, is a continuous variable with excess zeros and skewness. This outcome is complex because there are 3 possible activity patterns in each interval (inactive, partially active, and completely active), and those patterns, which are observed, induce both nonrandom and random associations between the responses. More specifically, the inactive pattern requires a zero value in both the proportion for active behavior and the energy expenditure rate; a partially active pattern means that the proportion of activity is strictly between zero and one and that the energy expenditure rate is greater than zero and likely to be moderate, and the completely active pattern means that the proportion of activity is exactly one, and the energy expenditure rate is greater than zero and likely to be higher. To address these challenges, we propose a 3-part functional data joint modeling approach. The first part is a continuation-ratio model to reorder the ordinal valued 3 activity patterns. The second part models the proportions when they are in interval (0,1). The last component specifies the skewed continuous energy expenditure rate with Box-Cox transformations when they are greater than zero. In this 3-part model, the regression structures are specified as smooth curves measured at various time points with random effects that have a correlation structure. The smoothed random curves for each variable are summarized using a few important principal components, and the association of the 3 longitudinal components is modeled through the association of the principal component scores. The difficulties in handling the ordinal and proportional variables are addressed using a quasi-likelihood type approximation. We develop an efficient algorithm to fit the model that also involves the selection of the number of principal components. The method is applied to physical activity data and is evaluated empirically by a simulation study.

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Joint modeling of longitudinal drug using pattern and time to first relapse in cocaine dependence treatment data

Cited by 8 publications

References 46 publications

Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease

Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease

Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time‐to‐event data

Three‐part joint modeling methods for complex functional data mixed with zero‐and‐one–inflated proportions and zero‐inflated continuous outcomes with skewness

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