Journal
BIOMETRIKA
Volume 92, Issue 4, Pages 875-891Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biomet/92.4.875
Keywords
cause-specific hazard; competing risks; doubly robust; inverse probability weighted; linear transformation model; missing at random
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We consider the problem of estimating the regression coefficients in a competing risks model, where the relationship between the cause-specific hazard for the cause of interest and covariates is described using linear transformation models and when cause of failure is missing at random for a subset of individuals. Using the theory of Robins et al. (1994) for missing data problems and the approach of Chen et al. (2002) for estimating regression coefficients for linear transformation models, we derive augmented inverse probability weighted complete-case estimators for the regression coefficients that are doubly robust. Simulations demonstrate the relevance of the theory in finite samples.
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