Abstract
A model for survival analysis is studied that is relevant for samples which are subject to multiple types of failure. In comparison with a more standard approach, through the appropriate use of hazard functions and transition probabilities, the model allows for a more accurate study of cause-specific failure with regard to both the timing and type of failure. A semiparametric specification of a mixture model is employed that is able to adjust for concomitant variables and allows for the assessment of their effects on the probabilities of eventual causes of failure through a generalized logistic model, and their effects on the corresponding conditional hazard functions by employing the Cox proportional hazards model. A carefully formulated estimation procedure is presented that uses an EM algorithm based on a profile likelihood construction. The methods discussed, which could also be used for reliability analysis, are applied to a prostate cancer data set.
Original language | English |
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Pages (from-to) | 277-293 |
Number of pages | 17 |
Journal | Communications in Statistics: Theory and Methods |
Volume | 37 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Jan 2008 |
Keywords
- product-limit estimate
- multiple decrement data
- split-population models
- EM algorithm
- event-specific hazard