Flexible survival regression modelling. Export

@article{citeulike:6072340, 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.}, author = {Cortese, Giuliana and Scheike, Thomas H. and Martinussen, Torben}, citeulike-article-id = {6072340}, citeulike-linkout-0 = {http://dx.doi.org/10.1177/0962280209105022}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19608605}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19608605}, day = {16}, doi = {10.1177/0962280209105022}, issn = {0962-2802}, journal = {Statistical methods in medical research}, keywords = {survival}, month = {July}, posted-at = {2009-11-05 04:26:43}, priority = {2}, title = {Flexible survival regression modelling.}, url = {http://dx.doi.org/10.1177/0962280209105022}, year = {2009} }

TY - JOUR ID - citeulike:6072340 L3 - citeulike-article-id:6072340 N2 - 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. JF - Statistical methods in medical research SN - 0962-2802 TI - Flexible survival regression modelling. KW - survival AU - Cortese, Giuliana AU - Scheike, Thomas AU - Martinussen, Torben PY - 2009/07/16/ UR - http://dx.doi.org/10.1177/0962280209105022 ER -