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In various data situations joint models are an efficient tool to analyzerelationships between time dependent covariates and event times or to correctfor event-dependent dropout occurring in regression analysis. Joint modelingconnects a longitudinal and a survival submodel within a single jointlikelihood which then can be maximized by standard optimization methods. Mainburdens of these conventional methods are that the computational effortincreases rapidly in higher dimensions and they do not offer special tools forproper variable selection. Gradient boosting techniques are well known amongstatisticians for addressing exactly these problems, hence an initial boostingalgorithm to fit a basic joint model based on functional gradient descentmethods has been proposed. Aim of this work is to extend this algorithm inorder to fit a model incorporating baseline covariates affecting solely thesurvival part of the model. The extended algorithm is evaluated based on lowand high dimensional simulation runs as well as a data set on AIDS patients,where the longitudinal submodel models the underlying profile of the CD4 cellcount which then gets included alongside several baseline covariates in thesurvival submodel.

Extension of the Gradient Boosting Algorithm for Joint Modeling of Longitudinal and Time-to-Event data