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Proportional Hazards Regression with Missing Covariates
Hua Yun Chen and Roderick J. A. Little
Journal of the American Statistical Association
Vol. 94, No. 447 (Sep., 1999), pp. 896-908
Stable URL: http://www.jstor.org/stable/2670005
Page Count: 13
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Nonparametric maximum likelihood (NPML) is used to estimate regression parameters in a proportional hazards regression model with missing covariates. The NPML estimator is shown to be consistent and asymptotically normally distributed under some conditions. EM type algorithms are applied to solve the maximization problem. Variance estimates of the regression parameters are obtained by a profile likelihood approach that uses EM-aided numerical differentiation. Simulation results indicate that the NPML estimates of the regression parameters are more efficient than the approximate partial likelihood estimates and estimates from complete-case analysis when missing covariates are missing completely at random, and that the proposed method corrects for bias when the missing covariates are missing at random.
Journal of the American Statistical Association © 1999 American Statistical Association