msSurv: Nonparametric Estimation for Multistate Models

Nicole Ferguson, Guy Brock and Somnath Datta have written an R package for non-parametric estimation in multi-state models. To some extent the package covers similar ground to mstate and etm, the focus being on data continuously observed up to right censoring. Unlike mstate there is no possibility of semi-parametric modelling. The main area of new functionality in msSurv is the ability to estimate state entry and exit time distributions, and the ability to cope with state dependent censoring mechanisms using the methodology of Datta and Satten (Biometrics, 2002). As with mstate, all computations appear to be performed within R itself. Thus if a standard Aalen-Johansen type estimate is required, etm is still the best package to use. For instance, the example simulated right censored data provided in the package takes over 3 minutes to fit using msSurv, compared to just 1.2 seconds in etm. Since, for the more bespoke parts of the package, e.g. robust estimates of state occupancy for non-Markov models or state dependent censoring, bootstrapping is required for confidence intervals, the lack of speed of msSurv is a little disappointing. Update: A paper on the msSurv package has now been published in the Journal of Statistical Software.