Prediction of cumulative incidence function under the proportional hazards model.
In the presence of dependent competing risks in survival analysis, the Cox model can be utilized to examine the covariate effects on the cause-specific hazard function for the failure type of interest. For this situation, the cumulative incidence function provides an intuitively appealing summary curve for marginal probabilities of this particular event. In this paper, we show how to construct confidence intervals and bands for such a function under the Cox model for future patients with certain covariates. Our proposals are illustrated with data from a prostate cancer trial.
Cheng
S C
SC
Department of Biomathematics, M. D. Anderson Cancer Center, University of Texas, Houston 77030, USA. scheng@odin.mdacc.tmc.edu
Fine
J P
JP
Wei
L J
LJ
D000368:Aged; D018931:Antineoplastic Agents, Hormonal / Q000008:administration & dosage / Q000627:therapeutic use; D002318:Cardiovascular Diseases / Q000150:complications / Q000401:mortality; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D016001:Confidence Intervals; D004054:Diethylstilbestrol / Q000008:administration & dosage / Q000627:therapeutic use; D006801:Humans; D008297:Male; D016016:Proportional Hazards Models; D011471:Prostatic Neoplasms / Q000150:complications / Q000188:drug therapy / Q000401:mortality; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012306:Risk; D016019:Survival Analysis
D018931:Antineoplastic Agents, Hormonal; D004054:Diethylstilbestrol