Parametric survival functions

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Parametric survival functions

Author Info

Patrick Royston () (University College London)

Abstract

Cox proportional-hazard regression has been essentially the automatic choice of analysis tool for modeling survival data in medical studies. However, the Cox model has several intrinsic features that may cause problems for the analyst or an interpreter of the data. These include the necessity of assuming proportional hazards and the very noisy estimate of the baseline hazard function that is typically obtained. I shall demonstrate flexible parametric models based on a proportional hazards or a proportional odds metric. I will show the utility of such models in helping one to visualize the hazard function and hazard ratio as functions of time, and in modeling data with non-proportional effects of some or all of the covariates. Series: United Kingdom Stata Users' Group Meeting, 2001

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Paper provided by Stata Users Group in its series United Kingdom Stata Users' Group Meetings 2001 with number 15.

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Date of creation: 25 Apr 2001
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Handle: RePEc:boc:usug01:15

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