Penalized estimation of semiparametric transformation models with interval-censored data and application to Alzheimer’s disease

Li, Shuwei; Wu, Qiwei; Sun, Jianguo

doi:10.1177/0962280219884720

Cited by 28 publications

(33 citation statements)

References 39 publications

(59 reference statements)

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“…(1) The results show that APOE4 is significantly associated with AD risk at level 0.05, which is consistent with existing AD studies. In particular, the presence of APOE4 allele increases the hazard of AD diagnosis by 39.10% while Our findings are again supported by an independent study (Li et al, 2019), which applied a penalized method and consistently selected RAVLT-immediate as the only significant risk factor of AD conversion. Figure 3 presents the estimated mean functions of longitudinal outcomes by three methods.…”

Section: Discussionsupporting

confidence: 84%

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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: Discussionsupporting

confidence: 84%

“…By contrast, RAVLT‐immediate contributes through the outcome‐specific progression in addition to the shared latent progression. Our findings are again supported by an independent study (Li et al ., 2019), which applied a penalized method and consistently selected RAVLT‐immediate as the only significant risk factor of AD conversion.…”

Section: Discussionmentioning

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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“…By contrast, RAVLT-immediate contributes through the outcome-specific progression in addition to the shared latent progression. Our findings are again supported by an independent study (Li et al, 2019), which applied a penalized method and consistently selected RAVLT-immediate as the only significant risk factor of AD conversion.…”

Section: Discussionsupporting

confidence: 83%

Joint Model for Survival and Multivariate Sparse Functional Data with Application to a Study of Alzheimer's Disease

Li,

Xiao,

Luo

2020

Preprint

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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 (MFMM) 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.

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“…As mentioned above, several authors have discussed variable selection for intervalcensored failure time data ( [19,20]). However, existing methods cannot be applied directly to linear models or generalized linear models.…”

Section: Introductionmentioning

confidence: 99%

Variable Selection for Generalized Linear Models with Interval-Censored Failure Time Data

Liu

Zhao

et al. 2022

Mathematics

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Variable selection is often needed in many fields and has been discussed by many authors in various situations. This is especially the case under linear models and when one observes complete data. Among others, one common situation where variable selection is required is to identify important risk factors from a large number of covariates. In this paper, we consider the problem when one observes interval-censored failure time data arising from generalized linear models, for which there does not seem to exist an established method. To address this, we propose a penalized least squares method with the use of an unbiased transformation and the oracle property of the method is established along with the asymptotic normality of the resulting estimators of regression parameters. Simulation studies were conducted and demonstrated that the proposed method performed well for practical situations. In addition, the method was applied to a motivating example about children’s mortality data of Nigeria.

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Penalized estimation of semiparametric transformation models with interval-censored data and application to Alzheimer’s disease

Cited by 28 publications

References 39 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

Joint Model for Survival and Multivariate Sparse Functional Data with Application to a Study of Alzheimer's Disease

Variable Selection for Generalized Linear Models with Interval-Censored Failure Time Data

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