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Model building in nonproportional hazard regression
Author(s) -
RodríguezGirondo Mar,
Kneib Thomas,
CadarsoSuárez Carmen,
AbuAssi Emad
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5961
Subject(s) - covariate , context (archaeology) , piecewise , computer science , proportional hazards model , model selection , regression , hazard , poisson regression , regression analysis , econometrics , exponential family , statistics , mathematics , machine learning , medicine , mathematical analysis , paleontology , population , chemistry , environmental health , organic chemistry , biology
Recent developments of statistical methods allow for a very flexible modeling of covariates affecting survival times via the hazard rate, including also the inspection of possible time‐dependent associations. Despite their immediate appeal in terms of flexibility, these models typically introduce additional difficulties when a subset of covariates and the corresponding modeling alternatives have to be chosen, that is, for building the most suitable model for given data. This is particularly true when potentially time‐varying associations are given. We propose to conduct a piecewise exponential representation of the original survival data to link hazard regression with estimation schemes based on of the Poisson likelihood to make recent advances for model building in exponential family regression accessible also in the nonproportional hazard regression context. A two‐stage stepwise selection approach, an approach based on doubly penalized likelihood, and a componentwise functional gradient descent approach are adapted to the piecewise exponential regression problem. These three techniques were compared via an intensive simulation study. An application to prognosis after discharge for patients who suffered a myocardial infarction supplements the simulation to demonstrate the pros and cons of the approaches in real data analyses. Copyright © 2013 John Wiley & Sons, Ltd.