title: Using the EM-algorithm for survival data with incomplete categorical covariates.
abstract: Incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With generalized linear models, when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights proposed in Ibrahim (1990). In this article, we extend the EM by the method of weights to survival outcomes whose distributions may not fall in the class of generalized linear models. This method requires the estimation of the parameters of the distribution of the covariates. We present a clinical trials example with five covariates, four of which have some missing values.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
authors.forename: S R
authors.initials: SR
authors.lastname: Lipsitz
authors.forename: J G
authors.initials: JG
authors.lastname: Ibrahim
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001699:Biometry; D003627:Data Interpretation, Statistical; D006801:Humans; D015993:Life Tables; D008113:Liver Neoplasms / Q000401:mortality / Q000628:therapy; D012044:Regression Analysis; D016019:Survival Analysis