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.

Afiliación

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 en En | MEDLINE | ID: mdl-38043523

Asunto(s)

Modelos Estadísticos; Humanos; Límite de Detección; Simulación por Computador; Estudios Longitudinales

Palabras clave

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

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Modelos Estadísticos Límite: Humans Idioma: En Revista: Stat Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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