To present a relatively novel method for modeling length-of-stay data and assess the role of covariates, some of which are related to adverse events. To undertake critical comparisons with alternative models based on the gamma and log-normal distributions. To demonstrate the effect of poorly fitting models on decision-making. The model has the process of hospital stay organized into Markov phases/states that describe stay in hospital before discharge to an absorbing state. Admission is via state 1 and discharge from this first state would correspond to a short stay, with transitions to later states corresponding to longer stays. The resulting phase-type probability distributions provide a flexible modeling framework for length-of-stay data which are known to be awkward and difficult to fit to other distributions. The dataset consisted of 1901 patients' lengths of stay and values for a number of covariates. The fitted model comprised six Markov phases, and provided a good fit to the data. Alternative gamma and log-normal models did not fit as well, gave different coefficient estimates, and statistical significance of covariate effects differed between the models. Models that fit should generally be preferred over those that do not, as they will produce more statistically reliable coefficient estimates. Poor coefficient estimates may mislead decision-makers by either understating or overstating the cost of some event or the cost savings from preventing that event. There is no obvious way of identifying a priori when coefficient estimates from poorly fitting models might be misleading.
