We present an approach for analysing internal dependencies in counting processes. This covers the case with repeated events on each of a number of individuals, and, more generally, the situation where several processes are observed for each individual. We define dynamic covariates, i.e. covariates depending on the past of the processes. The statistical analysis is performed mainly by the nonparametric additive approach. This yields a method for analysing multivariate
survival data, which is an alternative to the frailty approach. We present cumulative regression plots, statistical tests, residual plots and a hat matrix plot for studying outliers. A program in R and S-PLUS for analyzing survival data with the additive regression model is available on the web site http://www.med.uio.no/imb/english/research/groups/causal-inference-methods/software/
The program has been developed to fit the counting process framework.