title: Competing risks models and time-dependent covariates.
abstract: New statistical models for analysing survival data in an intensive care unit context have recently been developed. Two models that offer significant advantages over standard survival analyses are competing risks models and multistate models. Wolkewitz and colleagues used a competing risks model to examine survival times for nosocomial pneumonia and mortality. Their model was able to incorporate time-dependent covariates and so examine how risk factors that changed with time affected the chances of infection or death. We briefly explain how an alternative modelling technique (using logistic regression) can more fully exploit time-dependent covariates for this type of data.
authors.affiliation: Institute of Health and Biomedical Innovation, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove Urban Village, Kelvin Grove, Queensland 4059, Australia. a.barnett@qut.edu.au
authors.forename: Adrian
authors.initials: A
authors.lastname: Barnett
authors.forename: Nick
authors.initials: N
authors.lastname: Graves
mesh_terms: D003428:Cross Infection / Q000401:mortality*; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D007442:Intubation, Intratracheal; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D015233:Models, Statistical; D011014:Pneumonia / Q000401:mortality*; D011159:Population Surveillance; D012121:Respiration, Artificial; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D013514:Surgical Procedures, Operative