title: A full competing risk analysis of hospital-acquired infections can easily be performed by a case-cohort approach.
abstract: We provide a case-cohort approach and show that a full competing risk analysis is feasible even in a reduced data set. Competing events for hospital-acquired infections are death or discharge from the hospital because they preclude the observation of such infections.
  
  
  
  Using surveillance data of 6,568 patient admissions (full cohort) from two Spanish intensive care units, we propose a case-cohort approach which uses only data from a random sample of the full cohort and all infected patients (the cases). We combine established methodology to study following measures: event-specific as well as subdistribution hazard ratios for all three events (infection, death, and discharge), cumulative hazards as well as incidence functions by risk factor, and also for all three events.
  
  
  
  Compared with the values from the full cohort, all measures are well approximated with the case-cohort design. For the event of interest (infection), event-specific and subdistribution hazards can be estimated with the full efficiency of the case-cohort design. So, standard errors are only slightly increased, whereas the precision of estimated hazards of the competing events is inflated according to the size of the subcohort.
  
  
  
  The case-cohort design provides an appropriate sampling design for studying hospital-acquired infections in a reduced data set. Potential effects of risk factors on the competing events (death and discharge) can be evaluated.
authors.affiliation: Institute for Medical Biometry and Statistics, Center for Medical Biometry and Medical Informatics, Medical Center University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany. Electronic address: wolke@imbi.uni-freiburg.de.
authors.forename: Martin
authors.initials: M
authors.lastname: Wolkewitz
authors.affiliation: ICU Department University Hospital Arnau de Vilanova, Lleida, Spain and Universitat Autónoma de Barcelona, Spain.
authors.forename: Mercedes
authors.initials: M
authors.lastname: Palomar-Martinez
authors.affiliation: Service of Intensive Care Medicine, Hospital de Galdakao-Usansolo, Labeaga s/n. 48960. Galdakao, Bizkaia, Spain.
authors.forename: Pedro
authors.initials: P
authors.lastname: Olaechea-Astigarraga
authors.affiliation: Service of Intensive Care Medicine, Parc de Salut Mar, Universitat Autonoma de Barcelona IMIM (GREPAC - Grup Recerca Patologia Crítica) Passeig Marítim, 25-29. 08003 Barcelona, Spain.
authors.forename: Francisco
authors.initials: F
authors.lastname: Alvarez-Lerma
authors.affiliation: Institute for Medical Biometry and Statistics, Center for Medical Biometry and Medical Informatics, Medical Center University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Schumacher
mesh_terms: D015331:Cohort Studies; D003428:Cross Infection / Q000453:epidemiology*; D015340:Epidemiologic Research Design; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data; D015233:Models, Statistical; D010351:Patient Discharge / Q000706:statistics & numerical data*; D016016:Proportional Hazards Models; D018570:Risk Assessment; D012307:Risk Factors; D013030:Spain / Q000453:epidemiology
keywords: Competing risk model; Cox proportional hazards model; Cumulative incidence function; Fine and Gray model; Multistate model; Nosocomial infections; Sampling designs; Subdistribution