title: Proportional hazards model with covariates subject to measurement error.
abstract: When covariates of a proportional hazards model are subject to measurement error, the maximum likelihood estimates of regression coefficients based on the partial likelihood are asymptotically biased. Prentice (1982, Biometrika 69, 331-342) presents an example of such bias and suggests a modified partial likelihood. This paper applies the corrected score function method (Nakamura, 1990, Biometrika 77, 127-137) to the proportional hazards model when measurement errors are additive and normally distributed. The result allows a simple correction to the ordinary partial likelihood that yields asymptotically unbiased estimates; the validity of the correction is confirmed via a limited simulation study.
authors.affiliation: School of Allied Medical Sciences, Nagasaki University, Japan.
authors.forename: T
authors.initials: T
authors.lastname: Nakamura
mesh_terms: D000328:Adult; D000368:Aged; D000704:Analysis of Variance; D015982:Bias; D006801:Humans; D007564:Japan; D008433:Mathematics; D008875:Middle Aged; D009689:Nuclear Warfare; D016016:Proportional Hazards Models; D012044:Regression Analysis; D016019:Survival Analysis