Abstract
Regression analysis of survival data, and more generally event history data, is typically based on Coxgs regression model. We here review some recent methodology, focusing on the limitations of Coxgs 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 Coxgs regression model and the need for other supplementary analyses with models such as those we present here.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Statistical Methods in Medical Research |
| Vol/bind | 19 |
| Udgave nummer | 1 |
| Sider (fra-til) | 5-28 |
| Antal sider | 24 |
| ISSN | 0962-2802 |
| DOI | |
| Status | Udgivet - feb. 2010 |
| Udgivet eksternt | Ja |
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