@article{lu_tsiatis_2005, title={Comparison between two partial likelihood approaches for the competing risks model with missing cause of failure}, volume={11}, ISSN={["1572-9249"]}, DOI={10.1007/s10985-004-5638-0}, abstractNote={In many clinical studies where time to failure is of primary interest, patients may fail or die from one of many causes where failure time can be right censored. In some circumstances, it might also be the case that patients are known to die but the cause of death information is not available for some patients. Under the assumption that cause of death is missing at random, we compare the Goetgbebeur and Ryan (1995, Biometrika, 82, 821-833) partial likelihood approach with the Dewanji (1992, Biometrika, 79, 855-857) partial likelihood approach. We show that the estimator for the regression coefficients based on the Dewanji partial likelihood is not only consistent and asymptotically normal, but also semiparametric efficient. While the Goetghebeur and Ryan estimator is more robust than the Dewanji partial likelihood estimator against misspecification of proportional baseline hazards, the Dewanji partial likelihood estimator allows the probability of missing cause of failure to depend on covariate information without the need to model the missingness mechanism. Tests for proportional baseline hazards are also suggested and a robust variance estimator is derived.}, number={1}, journal={LIFETIME DATA ANALYSIS}, author={Lu, KF and Tsiatis, AA}, year={2005}, month={Mar}, pages={29–40} }
 @article{lu_tsiatis_2001, title={Multiple imputation methods for estimating regression coefficients in the competing risks model with missing cause of failure}, volume={57}, ISSN={["0006-341X"]}, DOI={10.1111/j.0006-341X.2001.01191.x}, abstractNote={Summary. We propose a method to estimate the regression coefficients in a competing risks model where the cause‐specific hazard for the cause of interest is related to covariates through a proportional hazards relationship and when cause of failure is missing for some individuals. We use multiple imputation procedures to impute missing cause of failure, where the probability that a missing cause is the cause of interest may depend on auxiliary covariates, and combine the maximum partial likelihood estimators computed from several imputed data sets into an estimator that is consistent and asymptotically normal. A consistent estimator for the asymptotic variance is also derived. Simulation results suggest the relevance of the theory in finite samples. Results are also illustrated with data from a breast cancer study.}, number={4}, journal={BIOMETRICS}, author={Lu, KF and Tsiatis, AA}, year={2001}, month={Dec}, pages={1191–1197} }