title: Semiparametric transformation models for semicompeting survival data.
abstract: Semicompeting risk outcome data (e.g., time to disease progression and time to death) are commonly collected in clinical trials. However, analysis of these data is often hampered by a scarcity of available statistical tools. As such, we propose a novel semiparametric transformation model that improves the existing models in the following two ways. First, it estimates regression coefficients and association parameters simultaneously. Second, the measure of surrogacy, for example, the proportion of the treatment effect that is mediated by the surrogate and the ratio of the overall treatment effect on the true endpoint over that on the surrogate endpoint, can be directly obtained. We propose an estimation procedure for inference and show that the proposed estimator is consistent and asymptotically normal. Extensive simulations demonstrate the valid usage of our method. We apply the method to a multiple myeloma trial to study the impact of several biomarkers on patients' semicompeting outcomes--namely, time to progression and time to death.
authors.affiliation: Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan, China.
authors.forename: Huazhen
authors.initials: H
authors.lastname: Lin
authors.affiliation: Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan, China.
authors.forename: Ling
authors.initials: L
authors.lastname: Zhou
authors.affiliation: Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
authors.forename: Chunhong
authors.initials: C
authors.lastname: Li
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
mesh_terms: D014408:Biomarkers, Tumor / Q000097:blood*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009101:Multiple Myeloma / Q000097:blood* / Q000175:diagnosis / Q000401:mortality*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity; D016019:Survival Analysis
chemical_list: D014408:Biomarkers, Tumor
keywords: Semicompeting risk data; Semiparametric linear transformation model; Surrogate endpoints