Ontology highlight
ABSTRACT: Background
In many studies the information of patients who are dying in the hospital is censored when examining the change in length of hospital stay (cLOS) due to hospital-acquired infections (HIs). While appropriate estimators of cLOS are available in literature, the existence of the bias due to censoring of deaths was neither mentioned nor discussed by the according authors.Methods
Using multi-state models, we systematically evaluate the bias when estimating cLOS in such a way. We first evaluate the bias in a mathematically closed form assuming a setting with constant hazards. To estimate the cLOS due to HIs non-parametrically, we relax the assumption of constant hazards and consider a time-inhomogeneous Markov model.Results
In our analytical evaluation we are ab
SUBMITTER: Rahman S
PROVIDER: S-EPMC5975458 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature