TY - JOUR
T1 - Checking Fine and Gray subdistribution hazards model with cumulative sums of residuals
AU - Li, Jianing
AU - Scheike, Thomas
AU - Zhang, Mei Jie
PY - 2015/4
Y1 - 2015/4
N2 - Recently, Fine and Gray (J Am Stat Assoc 94:496–509, 1999) proposed a semi-parametric proportional regression model for the subdistribution hazard function which has been used extensively for analyzing competing risks data. However, failure of model adequacy could lead to severe bias in parameter estimation, and only a limited contribution has been made to check the model assumptions. In this paper, we present a class of analytical methods and graphical approaches for checking the assumptions of Fine and Gray’s model. The proposed goodness-of-fit test procedures are based on the cumulative sums of residuals, which validate the model in three aspects: (1) proportionality of hazard ratio, (2) the linear functional form and (3) the link function. For each assumption testing, we provide a p-values and a visualized plot against the null hypothesis using a simulation-based approach. We also consider an omnibus test for overall evaluation against any model misspecification. The proposed tests perform well in simulation studies and are illustrated with two real data examples.
AB - Recently, Fine and Gray (J Am Stat Assoc 94:496–509, 1999) proposed a semi-parametric proportional regression model for the subdistribution hazard function which has been used extensively for analyzing competing risks data. However, failure of model adequacy could lead to severe bias in parameter estimation, and only a limited contribution has been made to check the model assumptions. In this paper, we present a class of analytical methods and graphical approaches for checking the assumptions of Fine and Gray’s model. The proposed goodness-of-fit test procedures are based on the cumulative sums of residuals, which validate the model in three aspects: (1) proportionality of hazard ratio, (2) the linear functional form and (3) the link function. For each assumption testing, we provide a p-values and a visualized plot against the null hypothesis using a simulation-based approach. We also consider an omnibus test for overall evaluation against any model misspecification. The proposed tests perform well in simulation studies and are illustrated with two real data examples.
KW - Competing risk
KW - Cumulative residual
KW - Goodness-of-fit
KW - Link function
KW - Omnibus test
KW - Proportional subdistribution hazard
UR - http://www.scopus.com/inward/record.url?scp=84925543439&partnerID=8YFLogxK
U2 - 10.1007/s10985-014-9313-9
DO - 10.1007/s10985-014-9313-9
M3 - Journal article
C2 - 25421251
AN - SCOPUS:84925543439
SN - 1380-7870
VL - 21
SP - 197
EP - 217
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
IS - 2
ER -