Book Chapter10.1007/978-3-030-88658-5_16
Bayesian Approach for Joint Modeling Longitudinal Data and Survival Data Simultaneously in Public Health Studies
John-Stewart Gordon
- 01 Jan 2022
- pp 343-355
About: The article was published on 01 Jan 2022. The article focuses on the topics: Bayesian probability & Proportional hazards model.
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References
•Book
Modeling Survival Data: Extending the Cox Model
Terry M. Therneau,Patricia Grambsch +1 more
- 11 Aug 2000
TL;DR: A Cox Model-based approach was used to estimate the Survival and Hazard Functions and the results confirmed the need for further investigation into the role of natural disasters in shaping survival rates.
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A joint model for survival and longitudinal data measured with error.
TL;DR: This work argues that the Cox proportional hazards regression model method is superior to naive methods where one maximizes the partial likelihood of the Cox model using the observed covariate values and improves on two-stage methods where empirical Bayes estimates of the covariate process are computed and then used as time-dependent covariates.
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Joint modelling of longitudinal measurements and event time data.
TL;DR: This paper formulates a class of models for the joint behaviour of a sequence of longitudinal measurements and an associated sequence of event times, including single-event survival data, using results from a clinical trial into the treatment of schizophrenia.
JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data
TL;DR: This paper presents the R package JM, a package JM that fits joint models for longitudinal and time-to-event data, and describes its use in longitudinal studies.