Change in length of hospital stay (LOS) is frequently used to assess the impact and the costs of hospital-acquired complications.
In order to compute the attributable change in LOS, it is crucial to account for the timing of events:
A complication can only have an effect on LOS, once it has occured. These temporal dynamics can be adequately handled by multistate models;
however, there is few software for such models available.
We introduce an R-package "changeLOS" for computing change in LOS based on methods described in Schulgen and Schumacher (1996).
We will illustrate the program on data from a prospective cohort study on hospital-acquired infections.
Main features of the R-package "changeLOS" are R-methods to:
(1) describe the multi-state model.
(2) compute the Aalen-Johansen estimator for the matrix of transition probabilities P(u-, u) for all observed transition times u.
(3) compute the Aalen-Johansen estimator for the matrix of transition probabilities P(s,t);
the estimator is a finite matrix product of matrices P(u-,u) for every observed event time in the interval(s,t].
(4) visualize the temporal dynamics of the data, illustrated by transition probabilities.
(5) compute and visualize change in LOS.
(6) compute bootstrap variances for change in LOS.

Change in length of hospital stay (LOS) is frequently used to assess the impact and the costs of hospital-acquired complications. In order to compute the attributable change in LOS, it is crucial to account for the timing of events: A complication can only have an effect on LOS, once it has occured. These temporal dynamics can be adequately handled by multistate models; however, there is few software for such models available. We introduce an R-package "changeLOS" for computing change in LOS based on methods described in Schulgen and Schumacher (1996). We will illustrate the program on data from a prospective cohort study on hospital-acquired infections. Main features of the R-package "changeLOS" are R-methods to: (1) describe the multi-state model. (2) compute the Aalen-Johansen estimator for the matrix of transition probabilities P(u-, u) for all observed transition times u.(3) compute the Aalen-Johansen estimator for the matrix of transition probabilities P(s,t); the estimator is a finite matrix product of matrices P(u-,u) for every observed event time in the interval(s,t]. (4) visualize the temporal dynamics of the data, illustrated by transition probabilities. (5) compute and visualize change in LOS. (6) compute bootstrap variances for change in LOS.