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Improving nested case-control studies to conduct a full competing-risks analysis for nosocomial infections

Published online by Cambridge University Press:  30 August 2018

Derek Hazard ,
Martin Schumacher ,
Mercedes Palomar-Martinez ,
Francisco Alvarez-Lerma ,
Pedro Olaechea-Astigarraga  and
Martin Wolkewitz
Derek Hazard*
Affiliation:
Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
Martin Schumacher
Affiliation:
Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
Mercedes Palomar-Martinez
Affiliation:
Hospital Universitari Arnau de Vilanova, Lleida, Universitat Autonoma de Barcelona, Barcelona, Spain
Francisco Alvarez-Lerma
Affiliation:
Service of Intensive Care Medicine, Parc de Salut Mar, Barcelona, Spain
Pedro Olaechea-Astigarraga
Affiliation:
Service of Intensive Care Medicine, Parc de Salut Mar, Barcelona, Spain
Martin Wolkewitz
Affiliation:
Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
*
Author for correspondence: Derek Hazard, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany. E-mail: hazard@imbi.uni-freiburg.de
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Abstract

Objective

Competing risks are a necessary consideration when analyzing risk factors for nosocomial infections (NIs). In this article, we identify additional information that a competing risks analysis provides in a hospital setting. Furthermore, we improve on established methods for nested case-control designs to acquire this information.

Methods

Using data from 2 Spanish intensive care units and model simulations, we show how controls selected by time-dynamic sampling for NI can be weighted to perform risk-factor analysis for death or discharge without infection. This extension not only enables hazard rate analysis for the competing risk, it also enables prediction analysis for NI.

Results

The estimates acquired from the extension were in good agreement with the results from the full (real and simulated) cohort dataset. The reduced dataset results averted any false interpretation common in a competing-risks setting.

Conclusions

Using additional information that is routinely collected in a hospital setting, a nested case-control design can be successfully adapted to avoid a competing risks bias. Furthermore, this adapted method can be used to reanalyze past nested case-control studies to enhance their findings.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 39 , Issue 10 , October 2018 , pp. 1196 - 1201
Copyright
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved 

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