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Flexible survival regression modelling

¹ Department of Statistical Sciences, University of Padova, Via Cesare Battisti 241/243, 35121 Padova, Italy
² Department of Biostatistics, University of Copenhagen, ster Farimagsgade 5B, P.O.B. 2099, DK-1014 Copenhagen K, Denmark
³ Department of Biostatistics, University of Southern Denmark, J. B. Winsløvsvej 9 B, DK-5000 Odense, Denmark

^* To whom correspondence should be addressed. E-mail: gcortese{at}stat.unipd.it.

	Abstract

Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varying effects. We start by considering classical and also more recent goodness-of-fit procedures for the Cox model that will reveal when the Cox model does not capture important aspects of the data, such as time-varying effects. We present recent regression models that are able to deal with and describe such time-varying effects. The introduced models are all applied to data on breast cancer from the Norwegian cancer registry, and these analyses clearly reveal the shortcomings of Cox's regression model and the need for other supplementary analyses with models such as those we present here.

First published on July 16, 2009
Statistical Methods in Medical Research 2009, doi:10.1177/0962280209105022


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