In many clinical studies where time to failure is of primary interest, patients may fail or die from one of many causes. In some circumstances, it may also be the case that patients are known to die but the cause of death is unavailable. We propose a method to estimate the regression coefficients in a proportional hazards model when cause of failure is missing for some individuals. We use multiple imputation procedures to impute missing causes 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. Results are illustrated with data from a breast cancer study.
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