title: Estimating equations with incomplete categorical covariates in the Cox model.
abstract: Incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. When a full likelihood is specified, a useful technique for obtaining parameter estimates is the EM algorithm. We propose a set of estimating equations to estimate the parameters of Cox's proportional hazards model when some covariate values are missing. These estimating equations can be solved by an algorithm similar to the EM algorithm. Because of the computational burden of finding a solution to these estimating equations, we propose obtaining parameter estimates via Monte Carlo methods. Asymptotic variances of the parameter estimates are also derived. We present a clinical trials example with three covariates, two of which have some missing values.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. lipsitz@biostat.harvard.edu
authors.forename: S R
authors.initials: SR
authors.lastname: Lipsitz
authors.forename: J G
authors.initials: JG
authors.lastname: Ibrahim
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D006801:Humans; D008113:Liver Neoplasms / Q000401:mortality; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D012044:Regression Analysis