American Statistical Association
It has become increasingly common to observe an event time of interest, usually referred to as a survival time, along with baseline and longitudinal covariates. Both the survival and covariate processes are of interest, so is the relationship between them. Due to several complications, traditional approaches, including the partial likelihood approach for the Cox proportional hazards model and rank based approaches for the accelerated failure time model, encounter difficulties when longitudinal covariates are involved in the modeling of survival times. Moreover, the longitudinal processes are often subject to informative dropout. Jointly modeling the survival and longitudinal data emerges as an effective way to overcome these difficulties.
So far, attention has focused on Cox models for the survival data. The accelerated failure time (AFT) model is an attractive alternative to the Cox model when the proportionality assumption fails to capture the relation between the survival time and its longitudinal covariates. The question of model selection between these two survival models can be addressed by enlarging both models to a broader class, which we term "extended hazard survival model." After a brief overview, we illustrate how to implement the joint modeling approach by maximizing a pseudo joint likelihood function where random effects are treated as missing data. A Monte Carlo EM algorithm is employed to estimate the unknown parameters, including the unknown baseline hazard function. Statistical inference and some challenging issues in joint modeling will also be discussed.
|Date:||Thursday, May 8, 2008|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Judith Jansen Conference Room
4th Floor - Room 425
New York, New York