Multi-state modelling

A new version of multistateutils has been released onto CRAN containing a few new features. I’ll give a quick overview of them here, but have a look at the vignette for more examples. msprep2 The first is a replacement for the mstate::msprep function that converts data into the long transition-specific format required for fitting multi-state models. msprep requires the input data to be a in a wide format, where each row corresponds to an individual and each possible state has a column for entry time and a status indicator.

I’ve just returned from the 2018 Survival Analysis for Junior Researchers conference in Leiden, fresh with inspiration and wishing I was a PhD student again to have the luxury of time and independence to research all the ideas in my head. As with previous years it was extremely well organised with a variety of interesting talks, I particularly enjoyed the sessions on Causal Inference and Dynamic Prediction and hope to incorporate some of what I’ve learnt into my work.

I’ve just released an R package for estimating transition probabilities from multi-state models onto Github, found at https://github.com/stulacy/RDES. It’s not a package with a large potential audience, so I don’t intend to release it onto CRAN, rather it has a highly specific role that I developed for my own use and thought it could prove useful for someone else. Essentially, it extends the simulation functionality offered by the fantastic flexsurv package for obtaining predicted outcomes from multi-state models.

In the last couple of months I’ve been teaching myself about multi-state survival models for use in an upcoming project. While I found the theoretical concepts relatively straight forward, I started having issues when I began to start implementing the models in software. There are many considerations to be made when building a multi-state model, such as: Convert the data into a suitable long format Deciding whether to use either parametric or semi-parametric models Different subsets of the available covariates can be selected for each of the transition hazards In addition, covariates can be forced to have the same hazard ratio on every transition There’s a choice to be made between clock-forward or clock-reset (semi-Markov models) time-scales The Markov assumption can be further violated by including the state arrival times as part of the transition hazard; this often has theoretical justification The baseline hazards can be kept stratified by transition, or certain ones can be assumed to be proportional Needless to say, actually building a model was very time consuming.