A Comparison of Statistical Modeling Strategies for Analyzing Length of Stay after CABG Surgery

Investigators in clinical research are often interested in determining the association between patient characteristics and post-operative length of stay (LOS). We examined the relative performance of seven different statistical strategies for analyzing LOS in a cohort of patients undergoing CABG surgery. We compared linear regression; linear regression with log-transformed length of stay; generalized linear models with the following distributions: Poisson, negative binomial, normal, and gamma; and semi-parametric survival models.Nine of twenty patient characteristics were found to be significantly associated with increased LOS in all models. The models disagreed upon the statistical significance of the association between the remaining patient characteristics and increased LOS. Generalized linear models with Poisson, negative binomial, and gamma distributions, and the Cox regression model demonstrated the greatest consistency. With the exception of Cox regression, all models had similar ability to predict length of stay in the actual data. However, the generalized linear models tended to have marginally lower prediction error than the linear models. Using four measures of prediction error, Cox regression had substantially higher prediction error than the other models. Generalized linear models were best able to predict patient length of stay in Monte Carlo simulations that were performed.Researchers should consider generalized linear models with normal, Poisson, or negative binomial distributions for predicting length of stay following CABG surgery. Post-operative length of stay is a complex phenomenon that is difficult to incorporate into a simple parametric model due to a small proportion of patients having very long lengths of stay.