Semiparametric competing risks regression under interval censoring using the R package intccr. (May 2019) - Park, Jun; Bakoyannis, Giorgos; Yiannoutsos, Constantin T.
Highlights: Interval-censored competing risks data are frequently encountered in cohort studies and clinical trials with time-to-event outcomes. The cumulative incidence function explicitly quantifies the prognosis of a particular event in the presence of competing risks. Currently, there is no available software to perform general semiparametric regression analysis of the cumulative incidence function with interval-censored competing risks data. The R package intccr fits a large class of semiparametric regression models for interval-censored competing risks data and provides both covariate effects and predicted cumulative incidence functions. The package 'intccr' provides researchers with a convenient and flexible tool for the analysis of the cumulative incidence function based on interval-censored competing risks data. Abstract: Background and Objective : Competing risk data are frequently interval-censored in real-world applications, that is, the exact event time is not precisely observed but is only known to lie between two time points such as clinic visits. This type of data requires special handling because the actual event times are unknown. To deal with this problem we have developed an easy-to-use open-source statistical software. Methods : An approach to perform semiparametric regression analysis of the cumulative incidence function with interval-censored competing risks data is the sieve maximum likelihood method based on B-splines. An important feature of this approach is that it does not impose restrictive parametric assumptions. Also, this methodology provides semiparametrically efficient estimates. Implementation of this methodology can be easily performed using our newR packageintccr . Results : TheR packageintccr performs semiparametric regression analysis of the cumulative incidence function based on interval-censored competing risks data. It supports a large class of models including the proportional odds and the Fine–Gray proportional subdistribution hazards model as special cases. It also provides the estimated cumulative incidence functions for a particular combination of covariate values. The package also provides some data management functionality to handle data sets which are in a long format involving multiple lines of data per subject. Conclusions : TheR packageintccr provides a convenient and flexible software for the analysis of the cumulative incidence function based on interval-censored competing risks data.