Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group
نویسندگان
چکیده
منابع مشابه
Bayesian Sample Size Determination for Joint Modeling of Longitudinal Measurements and Survival Data
A longitudinal study refers to collection of a response variable and possibly some explanatory variables at multiple follow-up times. In many clinical studies with longitudinal measurements, the response variable, for each patient is collected as long as an event of interest, which considered as clinical end point, occurs. Joint modeling of continuous longitudinal measurements and survival time...
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1 Department of Statistics, The University of British Columbia, Vancouver, BC, Canada V6T 1Z2 2 Department of Mathematics and Statistics, York University, Toronto, ON, Canada M3J 1P3 3 Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada N2L 3G1 4 Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, F...
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In many biomedical studies, it is of interest to study the covariate effect on both longitudinal categorical outcomes and survival outcomes. For example, in cancer research, it is of interest to study the treatment effect on both quality of life which is a categorical outcome measured longitudinally and survival time. In this talk, we will discuss such joint models. Random effects are introduce...
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Vaccines have received a great deal of attention recently as potential therapies in cancer clinical trials. One reason for this is that they are much less toxic than chemotherapies and potentially less expensive. However, little is currently known about the biologic activity of vaccines and whether they are associated with clinical outcome. The antibody immune measures IgG and IgM have been pro...
متن کاملJoint modeling of survival and longitudinal data: likelihood approach revisited.
The maximum likelihood approach to jointly model the survival time and its longitudinal covariates has been successful to model both processes in longitudinal studies. Random effects in the longitudinal process are often used to model the survival times through a proportional hazards model, and this invokes an EM algorithm to search for the maximum likelihood estimates (MLEs). Several intriguin...
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ژورنال
عنوان ژورنال: Statistics in Medicine
سال: 2014
ISSN: 0277-6715
DOI: 10.1002/sim.6141