A multiple imputation approach to Cox regression with interval-censored data.
We propose a general semiparametric method based on multiple imputation for Cox regression with interval-censored data. The method consists of iterating the following two steps. First, from finite-interval-censored (but not right-censored) data, exact failure times are imputed using Tanner and Wei's poor man's or asymptotic normal data augmentation scheme based on the current estimates of the regression coefficient and the baseline survival curve. Second, a standard statistical procedure for right-censored data, such as the Cox partial likelihood method, is applied to imputed data to update the estimates. Through simulation, we demonstrate that the resulting estimate of the regression coefficient and its associated standard error provide a promising alternative to the nonparametric maximum likelihood estimate. Our proposal is easily implemented by taking advantage of existing computer programs for right-censored data.
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University of Minnesota-School of Public Health, Division of Biostatistics, Minneapolis 55455-0392, USA. weip@biostat.umn.edu
D001699:Biometry; D003627:Data Interpretation, Statistical; D006801:Humans; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D016019:Survival Analysis