ArticleThe annals of applied statistics2024
JOINT MODELING OF MULTISTATE AND NONPARAMETRIC MULTIVARIATE LONGITUDINAL DATA.
Article in The annals of applied statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Risk estimation and dynamic prediction using discrete-time joint models for longitudinal and multistate data with interval and state censoring.Biostatistics (Oxford, England) · 2026Article
- Co-occurrence of anaemia and malaria among children under five in Uganda: A joint modeling analysis.PLOS global public health · 2026Article
- Predictors of Transitions From GADA as the Initial Autoantibody to Multiple Autoantibodies of Type 1 Diabetes in Children at Risk by a Dynamic Prediction Model.Pediatric diabetes · 2025Article
- JOINT MODELING OF MULTISTATE AND NONPARAMETRIC MULTIVARIATE LONGITUDINAL DATA.The annals of applied statistics · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
It is oftentimes the case in studies of disease progression that subjects can move into one of several disease states of interest. Multistate models are an indispensable tool to analyze data from such studies. The Environmental Determinants of Diabetes in the Young (TEDDY) is an observational study of at-risk children from birth to onset of type-1 diabetes (T1D) up through the age of 15. A joint model for simultaneous inference of multistate and multivariate nonparametric longitudinal data is proposed to analyze data and answer the research questions brought up in the study. The proposed method allows us to make statistical inferences, test hypotheses, and make predictions about future state occupation in the TEDDY study. The performance of the proposed method is evaluated by simulation studies. The proposed method is applied to the motivating example to demonstrate the capabilities of the method.
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Registered trials
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