Estimating summary functionals in multistate models with an application to hospital infection data

Arthur Allignol, Martin Schumacher and Jan Beyersmann have a new paper in Computational Statistics. This considers methods for obtaining outcome measures based on estimates of the cumulative transition intensities in nonhomogeneous Markov models. Specifically they consider hospital length of stay data and consider estimating the expected excess time spent in hospital given an infection has occurred by time s, compared to if no infection has occurred by time s. For nonhomogeneous models this is clearly a function of the time s. They consider possible weighting methods to get a reasonable summary measure.

The plausibility of a Markov model is tested informally by including time since infection as a time dependent covariate in a Cox PH model. Some discussion of the extension of the methods to the non-Markov case is given.