Mstate: Data preparation, estimation and prediction in multi-state models. R package.

A significant barrier to the widespread use of multi-state models in applied statistics has been the lack of software. For right-censored data, models on the transition intensities can be fitted straightforwardly using standard survival modelling techniques (e.g. Cox regression and Nelson-Aalen estimators). However, for estimates of cumulative incidence functions, state occupation probabilities and moreover their standard errors, with a few exceptions it was generally necessary to make your own code. Hein Putter, Marta Fiocco and Liesbeth de Wreede have created the R package mstate, this provides a general framework for fitting right-censored and left-truncated non-parametric and semi-parametric multi-state models. The package exploits the existing R package survival to fit the models to intensities but also provides routines to calculate transition probabilities and their standard errors of the overall multi-state model. This is clearly a very useful tool. One small drawback of the package is that the routines such as those to calculate the transition probabilities appear to be coded entirely in R. As a result computation is the not as fast as might be hoped. The package etm by Arthur Allignol, which only computes the Aalen-Johansen estimator, may be preferable in terms of speed when only a non-parametric model is required as this incorporates some C code.

Update: An article on mstate in Computer Methods and Programs in Biomedicine is now available.

Further Update: A further paper on mstate is now available in the Journal of Statistical Software.