Regression analysis of current status data with latent variables

Wang, Chunjie; Zhao, Bo; Luo, Linlin; Song, Xinyuan

doi:10.1007/s10985-021-09521-9

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…General linear regression analysis estimates relations between one dependent variable and one independent variable in a straight line. Multiple linear regression analysis estimates relations between at least two independent variables and one dependent variable [19,20]. However, in reality, an independent variable is influenced by different variables, or there are multiple dependent variables.…”

Section: B Feature Extraction Using Multi-context Miningmentioning

confidence: 99%

Multi-Context Mining-Based Graph Neural Network for Predicting Emerging Health Risks

Baek

Chung

2023

IEEE Access

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Patients with similar diseases are able to have similar treatments, care, symptoms, and causes. Based on these relations, it is possible to predict latent risks. Therefore, this study proposes Graph Neural Network-based Multi-Context mining for predicting emerging health risks. The proposed method collects and pre-processes chronic disease patients' disease information, behavioral pattern information, and mental health information. After that, it performs context mining. This is a multivariate regression analysis for predicting multiple dependent variables, it extracts a regression model and generates a feature map. Then, the initial graph is created by defining the number of clusters as nodes and constructing edges through correlation. By expanding the graph according to the results of context mining, it is possible to predict that a user has a similar chronic disorder and similar symptoms through users' connection relations. For performance evaluation, the validity of the regression analysis of context mining used in the proposed method, and the suitability of the clustering technique are evaluated.

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Section: B Feature Extraction Using Multi-context Miningmentioning

confidence: 99%

Multi-Context Mining-Based Graph Neural Network for Predicting Emerging Health Risks

Baek

Chung

2023

IEEE Access

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Efficient estimation for accelerated failure time model with interval-censored data in the presence of a cured subgroup

Zhao

Wang

2022

Communications in Statistics - Simulation and Computation

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Factor-augmented transformation models for interval-censored failure time data

Li,

Sun

et al. 2024

Biometrics

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Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This work proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates. We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event. Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation. We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study is provided. An R package ICTransCFA is also available for practitioners. Data used in preparation of this article were obtained from the ADNI database.

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Regression analysis of current status data with latent variables

Cited by 4 publications

References 25 publications

Multi-Context Mining-Based Graph Neural Network for Predicting Emerging Health Risks

Multi-Context Mining-Based Graph Neural Network for Predicting Emerging Health Risks

Efficient estimation for accelerated failure time model with interval-censored data in the presence of a cured subgroup

Factor-augmented transformation models for interval-censored failure time data

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