1 Section of Biostatistics, Department of Public Health, Faculty of Health and Medical Sciences, Københavns Universitet2 Division of Biostatistics, Medical College of Wisconsin3 Division of Biostatistics, Medical College of Wisconsin4 Section of Biostatistics, Department of Public Health, Faculty of Health and Medical Sciences, Københavns Universitet
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.
Lifetime Data Analysis, 2015, Vol 21, Issue 2, p. 197-217
Competing risk; Cumulative residual; Goodness-of-fit; Link function; Omnibus test; Proportional subdistribution hazard