UNLABELLED: BACKGROUND/STUDY CONTEXT: It has not been previously demonstrated whether Bayesian joint modeling (BJM) of disability and survival can, under certain conditions, improve precision of individual survival curves. METHODS: A longitudinal, observational study wherein 754 initially nondisabled community-dwelling adults in greater New Haven, Connecticut, were observed on a monthly basis for over 10 years. RESULTS: In this study, BJM exploited many monthly observations to demonstrate, relative to a separate survival model with adjustment, improved precision of individual survival curves, permitting detection of significant differences between survival curves of two similar individuals. The gain in precision was lost when using only those observations from intervals of 6, 9, or 12 months. CONCLUSION: When there are many repeated measures, BJM of longitudinal functional disability and interval-censored survival can potentially increase the precision of individual survival curves relative to those from a separate survival model. This may facilitate the identification of significant differences between individual survival curves, a useful result usually precluded by the large variability inherent to individual-level estimates from stand-alone survival models.
UNLABELLED: BACKGROUND/STUDY CONTEXT: It has not been previously demonstrated whether Bayesian joint modeling (BJM) of disability and survival can, under certain conditions, improve precision of individual survival curves. METHODS: A longitudinal, observational study wherein 754 initially nondisabled community-dwelling adults in greater New Haven, Connecticut, were observed on a monthly basis for over 10 years. RESULTS: In this study, BJM exploited many monthly observations to demonstrate, relative to a separate survival model with adjustment, improved precision of individual survival curves, permitting detection of significant differences between survival curves of two similar individuals. The gain in precision was lost when using only those observations from intervals of 6, 9, or 12 months. CONCLUSION: When there are many repeated measures, BJM of longitudinal functional disability and interval-censored survival can potentially increase the precision of individual survival curves relative to those from a separate survival model. This may facilitate the identification of significant differences between individual survival curves, a useful result usually precluded by the large variability inherent to individual-level estimates from stand-alone survival models.
Authors: Julie A Womack; Terrence E Murphy; Linda Leo-Summers; Jonathan Bates; Samah Jarad; Alexandria C Smith; Thomas M Gill; Evelyn Hsieh; Maria C Rodriguez-Barradas; Phyllis C Tien; Michael T Yin; Cynthia A Brandt; Amy C Justice Journal: J Acquir Immune Defic Syndr Date: 2022-10-01 Impact factor: 3.771
Authors: Maha Alsefri; Maria Sudell; Marta García-Fiñana; Ruwanthi Kolamunnage-Dona Journal: BMC Med Res Methodol Date: 2020-04-26 Impact factor: 4.615
Authors: Julie A Womack; Terrence E Murphy; Christine Ramsey; Harini Bathulapalli; Linda Leo-Summers; Alexandria C Smith; Jonathan Bates; Samah Jarad; Thomas M Gill; Evelyn Hsieh; Maria C Rodriguez-Barradas; Phyllis C Tien; Michael T Yin; Cynthia Brandt; Amy C Justice Journal: J Acquir Immune Defic Syndr Date: 2021-10-01 Impact factor: 3.771