A full competing risk analysis of hospital-acquired infections can easily be performed by a case-cohort approach

Martin Wolkewitz, Mercedes Palomar-Martinez, Pedro Olaechea-Astigarraga, Francisco Alvarez-Lerma, Martin Schumacher

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)

Abstract

© 2015 Elsevier Inc. All rights reserved. Objectives 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. Study Design and Setting 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. Results 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. Conclusion 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.
Original languageEnglish
Pages (from-to)187-193
JournalJournal of Clinical Epidemiology
Volume74
DOIs
Publication statusPublished - 1 Jun 2016

Keywords

  • Competing risk model
  • Cox proportional hazards model
  • Cumulative incidence function
  • Fine and Gray model
  • Multistate model
  • Nosocomial infections
  • Sampling designs
  • Subdistribution

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