Using EM and Data Augmentation for the Competing Risks Model

Craiu, Radu V.; Duchesne, Thierry

doi:10.1002/0470090456.ch22

Masked Failure Data: Competing Risks

Basu

¹

2007

Encyclopedia of Statistics in Quality and Reliability

1

0

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The competing‐risks model considers a setting when a system is exposed to multiple risks and a system failure can be caused by one of the multiple risks. The case of masked causes arises when the causes of failure for some of the systems are not completely identified but are narrowed down to a subset of the potential risks. This article provides a selective review of statistical models and methods for analysis of competing‐risks data with or without masking in the context of reliability applications. Latent failure times and cause‐specific hazards provide two parallel approaches to competing‐risks models; identifiability issues in these models in both nonparametric and parametric settings are discussed. For the case of masked data, recent developments in both likelihood‐based and Bayesian approaches are reviewed. A special emphasis is given to the treatment and models for the masking probabilities. The article concludes with a review of different reliability applications.

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Masked Failure Data: Competing Risks

Basu

¹

2014

Wiley StatsRef: Statistics Reference Online

0

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The competing‐risks model considers a setting when a system is exposed to multiple risks and a system failure can be caused by one of the multiple risks. The case of masked causes arises when the causes of failure for some of the systems are not completely identified but are narrowed down to a subset of the potential risks. This article provides a selective review of statistical models and methods for analysis of competing‐risks data with or without masking in the context of reliability applications. Latent failure times and cause‐specific hazards provide two parallel approaches to competing‐risks models; identifiability issues in these models in both nonparametric and parametric settings are discussed. For the case of masked data, recent developments in both likelihood‐based and Bayesian approaches are reviewed. A special emphasis is given to the treatment and models for the masking probabilities. The article concludes with a review of different reliability applications.

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Inference for the Dependent Competing Risks Model with Masked Causes of Failure

Craiu

¹

,

Reiser

²

2006

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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.

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Using EM and Data Augmentation for the Competing Risks Model

Cited by 5 publications

References 34 publications

Masked Failure Data: Competing Risks

Masked Failure Data: Competing Risks

Masked Failure Data: Competing Risks

Inference for the Dependent Competing Risks Model with Masked Causes of Failure

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