Goftte: a R package for assessing goodness-of-fit in proportional (sub) distributions hazards regression models

Abstract Background and objective In this paper, we introduce a new R package goftte for goodness-of-fit assessment based on cumulative sums of model residuals useful for checking key assumptions in the Cox regression and Fine and Gray regression models. Methods Monte-Carlo methods are used to approximate the null distribution of cumulative sums of model residuals. To limit the computational burden, the main routines used to approximate the null distributions are implemented in a parallel C++ programming environment. Numerical studies are carried out to evaluate the empirical type I error rates of the different testing procedures. The package and the documentation are available to users from CRAN R repositories. Results Results from simulation studies suggested that all statistical tests implemented in goftte yielded excellent control of the type I error rate even with modest sample sizes with high censoring rates. Conclusions As compared to other R packages goftte provides new useful method for testing functionals, such as Anderson-Darling type test statistics for checking assumptions about proportional (sub-) distribution hazards. Approximations for the null distributions of test statistics have been validated through simulation experiments. Future releases will provide similar tools for checking model assumptions in multiplicative intensity models for recurrent data. The package may help to spread the use of recent advocated goodness-of-fit techniques in semiparametric regression for time-to-event data.