TY - JOUR
T1 - Fine-Gray proportional subdistribution hazards model for competing risks data under length-biased sampling
AU - Zhang, Feipeng
AU - Peng, Heng
AU - Zhou, Yong
N1 - Funding Information:
The authors are grateful to the editor and two anonymous referees for many helpful comments. Feipeng Zhang’s work is partially supported by the National Natural Science Foundation of China (11771133,11401194). Heng Peng’s research is supported in part by CEGR grant of the Research Grants Council of Hong Kong (No. HKBU 12302615 and HKBU 12303618), FRG grants from Hong Kong Baptist University (FRG2/16-17/042), and the Natural Science Foundation of Hunan Province, China (2017JJ3021). Yong Zhou’s work is partially supported by the State Key Program of National Natural Science Foundation of China (71331006), the State Key Program in the Major Research Plan of National Natural Science Foundation of China (91546202).
Funding Information:
The authors are grateful to the editor and two anonymous referees for many helpful comments. Feipeng Zhang's work is partially supported by the National Natural Science Foundation of China (11771133,11401194). Heng Peng's research is supported in part by CEGR grant of the Research Grants Council of Hong Kong (No. HKBU 12302615 and HKBU 12303618), FRG grants from Hong Kong Baptist University (FRG2/16-17/042), and the Natural Science Foundation of Hunan Province, China (2017JJ3021). Yong Zhou's work is partially supported by the State Key Program of National Natural Science Foundation of China (71331006), the State Key Program in the Major Research Plan of National Natural Science Foundation of China (91546202)
PY - 2019/1
Y1 - 2019/1
N2 - In this paper, we study the Fine-Gray proportional subdistribution hazards model for the competing risks data under length-biased sampling. To exploit the special structure of length-biased sampling, we propose an unbiased estimating equation estimator, which can handle both covariateindependent censoring and the covariate-dependent censoring. The large sample properties of the proposed estimator are derived, model-checking techniques for the model adequacy are developed, and the pointwise confidence intervals and the simultaneous confidence bands for the predicted cumulative incidence functions are also constructed. Simulation studies are conducted to assess the finite sample performance of the proposed estimator. An application to the employment data illustrates the method and theory.
AB - In this paper, we study the Fine-Gray proportional subdistribution hazards model for the competing risks data under length-biased sampling. To exploit the special structure of length-biased sampling, we propose an unbiased estimating equation estimator, which can handle both covariateindependent censoring and the covariate-dependent censoring. The large sample properties of the proposed estimator are derived, model-checking techniques for the model adequacy are developed, and the pointwise confidence intervals and the simultaneous confidence bands for the predicted cumulative incidence functions are also constructed. Simulation studies are conducted to assess the finite sample performance of the proposed estimator. An application to the employment data illustrates the method and theory.
KW - Competing risks data
KW - Fine-Gray model
KW - Lengthbiased sampling
KW - Model checking techniques
UR - http://www.scopus.com/inward/record.url?scp=85058173334&partnerID=8YFLogxK
U2 - 10.4310/SII.2019.v12.n1.a10
DO - 10.4310/SII.2019.v12.n1.a10
M3 - Journal article
AN - SCOPUS:85058173334
SN - 1938-7989
VL - 12
SP - 107
EP - 122
JO - Statistics and its Interface
JF - Statistics and its Interface
IS - 1
ER -