Joint modeling approaches for censored predictors due to detection limits with applications to metabolites data.

Ye, Peng; Bai, Shuo; Tang, Wan; Feng, Han; Qiao, Xinhua; Tu, Shengjia; He, Hua

Ye, Peng; Bai, Shuo; Tang, Wan; Feng, Han; Qiao, Xinhua; Tu, Shengjia; He, Hua.

Afiliação

Ye P; School of Statistics, University of International Business and Economics, Beijing, China.
Bai S; Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.
Tang W; Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.
Feng H; Tulane Research and Innovation for Arrhythmia Discovery- TRIAD Center, School of Medicine, Tulane University, New Orleans, Louisiana, USA.
Qiao X; School of Statistics, University of International Business and Economics, Beijing, China.
Tu S; Division of Biostatistics and Bioinformatics Herbert Wertheim School of Public Health and Human Longevity Science, La Jolla, California, USA.
He H; Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.

Stat Med ; 43(4): 674-688, 2024 02 20.

Article em En | MEDLINE | ID: mdl-38043523

Assuntos

Modelos Estatísticos; Humanos; Limite de Detecção; Simulação por Computador; Estudos Longitudinais

Palavras-chave

Bogalusa Heart Study; Tobit model; censored data; detection limit; joint modeling; latent class; mTobit model

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Estatísticos Limite: Humans Idioma: En Revista: Stat Med Ano de publicação: 2024 Tipo de documento: Article

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