Abstract
The competing risks model is useful in settings in which individuals/units may die/fail for different reasons. The cause specific hazard rates are taken to be piecewise constant functions. A complication arises when some of the failures are masked within a group of possible causes. Traditionally, statistical inference is performed under the assumption that the failure causes act independently on each item. In this paper we propose an EM-based approach which allows for dependent competing risks and produces estimators for the sub-distribution functions. We also discuss identifiability of parameters if none of the masked items have their cause of failure clarified in a second stage analysis (e.g. autopsy). The procedures proposed are illustrated with two datasets.
| Original language | English |
|---|---|
| Pages (from-to) | 21-33 |
| Number of pages | 13 |
| Journal | Lifetime Data Analysis |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| State | Published - Mar 2006 |
Bibliographical note
Funding Information:Acknowledgements We would like to thank Anup Dewanji and Debasis Sengupta for making their data available to us. We also would like to thank the Associate Editor and one referee for a set of thorough and helpful suggestions. The first author’s research was supported by an individual operating grant from the Natural Sciences and Engineering Research Council of Canada and an individual Connaught grant.
Keywords
- Dependent competing risks
- Masked cause
- Missing data
- Piecewise constant hazard
- Second stage data
ASJC Scopus subject areas
- Applied Mathematics