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A single‐index threshold Cox proportional hazard model for identifying a treatment‐sensitive subset based on multiple biomarkers
Author(s) -
He Ye,
Lin Huazhen,
Tu Dongsheng
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7837
Subject(s) - proportional hazards model , estimator , statistics , hazard , hazard ratio , mathematics , likelihood function , computer science , medicine , oncology , maximum likelihood , biology , confidence interval , ecology
In this paper, we introduce a single‐index threshold Cox proportional hazard model to select and combine biomarkers to identify patients who may be sensitive to a specific treatment. A penalized smoothed partial likelihood is proposed to estimate the parameters in the model. A simple, efficient, and unified algorithm is presented to maximize this likelihood function. The estimators based on this likelihood function are shown to be consistent and asymptotically normal. Under mild conditions, the proposed estimators also achieve the oracle property. The proposed approach is evaluated through simulation analyses and application to the analysis of data from two clinical trials, one involving patients with locally advanced or metastatic pancreatic cancer and one involving patients with resectable lung cancer.