title: Semiparametric estimation in the proportional hazard model accounting for a misclassified cause of failure.
abstract: Misclassified causes of failures are a common phenomenon in competing risks survival data such as cancer mortality. We propose new estimating equations for a semiparametric proportional hazards (PH) model with misattributed causes of failures. Unlike other methods, the estimator does not require any parametric assumptions on baseline cause-specific hazard rates. It is shown that the estimators for regression coefficients are consistent and asymptotically normal. Simulation results support the theoretical analysis in finite samples. The methods are applied to analyze prostate cancer survival.
authors.affiliation: Int Med-Geriatric Medicine, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
authors.forename: Jinkyung
authors.initials: J
authors.lastname: Ha
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
authors.forename: Alexander
authors.initials: A
authors.lastname: Tsodikov
mesh_terms: D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D016016:Proportional Hazards Models; D011471:Prostatic Neoplasms / Q000401:mortality; D016019:Survival Analysis
keywords: Competing risks; Estimating equations; Misattribution of cause of failure