Empirical Transition Matrix of Multi-State Models: The etm Package

Multi-State models provide a relevant framework for modelling complex event histories. Quantities of interest are the transition probabilities that can be estimated by the empirical transition matrix, that is also referred to as the Aalen-Johansen estimator. In this paper, we present the R package etm that computes and displays the transition probabilities. etm also features a Greenwood-type estimator of the covariance matrix. The use of the package is illustrated through a prominent example in bone marrow transplant for leukaemia patients.

Empirical Transition Matrix of Multi-State Models: The etm Package

Multi-State models provide a relevant framework for modelling complex event histories. Quantities of interest are the transition probabilities that can be estimated by the empirical transition matrix, that is also referred to as the Aalen-Johansen estimator. In this paper, we present the R package etm that computes and displays the transition probabilities. etm also features a Greenwood-type estimator of the covariance matrix. The use of the package is illustrated through a prominent example in bone marrow transplant for leukaemia patients

A competing risks approach for nonparametric estimation of transition probabilities in a non-Markov illness-death model

Competing risks model time to first event and type of first event. An example from hospital epidemiology is the incidence of hospital-acquired infection, which has to account for hospital discharge of non-infected patients as a competing risk. An illness-death model would allow to further study hospital outcomes of infected patients. Such a model typically relies on a Markov assumption. However, it is conceivable that the future course of an infected patient does not only depend on the time since hospital admission and current infection status but also on the time since infection. We demonstrate how a modified competing risks model can be used for nonparametric estimation of transition probabilities when the Markov assumption is violated

Regression modelling in hospital epidemiology: a statistical note

Barnett and Graves [1], in their commentary on our report recently published in Critical Care [2], suggested that timediscrete methods should be used to address time-dependent risk factors and competing risks. In this letter we comment on two statements by those authors. First, Barnett and Graves claim that, ‘An alternative method to the competing risks model is a multistate model. ’ In fact, a multistate model is not an alternative to modelling competing risks, but a competing risks model is an example of a multistate model. This is explained in the tutorial by Putter and coworkers [3]. However, competing risks only model the time to first event and the event type (for example, time to nosocomial infection [NI]) or discharge/death, whatever comes first. To model subsequent events also, more complex multistate models are needed. Barnett and Graves give a