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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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References

1. Wolkewitz, M, Cooper, BS, Bonten, MJM, Barnett, AG, Schumacher, M. Interpreting and comparing risks in the presence of competing events. BMJ. 2014;349:g5060.Google Scholar
2. Andersen, PK, Geskus, RB, de Witte, T, Putter, H. Competing risks in epidemiology: possibilities and pitfalls. Int J Epidemiol 2012;41:861–870.Google Scholar
3. Wolkewitz, M. Accounting for competing events in multivariate analyses of hospital-acquired infection risk factors. Infect Control Hosp Epidemiol 2016;37:1122–1124.Google Scholar
4. Weber, S, Cube, M von, Sommer, H, Wolkewitz, M. Necessity of a competing risk approach in risk factor analysis of central line–associated bloodstream infection. Infect Control Hosp Epidemiol 2016;37:1255–1257.Google Scholar
5. Aiken, AM, Mturi, N, Njuguna, P, et al. Risk and causes of paediatric hospital-acquired bacteraemia in Kilifi District Hospital, Kenya: a prospective cohort study. Lancet 2011;378:2021–2027.Google Scholar
6. Schumacher, M, Allignol, A, Beyersmann, J, Binder, N, Wolkewitz, M. Hospital-acquired infections—appropriate statistical treatment is urgently needed! Int J Epidemiol 2013;42:1502–1508.Google Scholar
7. Wolkewitz, M, von Cube, M, Schumacher, M. Multistate modeling to analyze nosocomial infection data: an introduction and demonstration. Infect Control Hosp Epidemiol 2017;38:953–959.Google Scholar
8. Obadia, T, Opatowski, L, Temime, L, et al. Interindividual contacts and carriage of methicillin-resistant staphylococcus aureus: a nested case-control study. Infect Control Hosp Epidemiol 2015;36:922–929.Google Scholar
9. O’Fallon, E, Kandell, R, Schreiber, R, D’Agata, E. Acquisition of multidrug-resistant gram- negative bacteria: incidence and risk factors within a long-term care population. Infect Control Hosp Epidemiol. 2010;31:1148–1153.Google Scholar
10. Samuelsen, SO. A pseudolikelihood approach to analysis of nested case-control studies. Biometrika 1997;84:379–394.Google Scholar
11. Saarela, O, Kulathinal, S, Arjas, E, Läärä, E. Nested case–control data utilized for multiple outcomes: a likelihood approach and alternatives. Statist Med 2008;27:5991–6008.Google Scholar
12. Cerceo, E, Lautenbach, E, Linkin, D, Bilker, WB, Lee, I. The role of matching in case-control studies of antimicrobial resistance. Infect Control Hosp Epidemiol 2009;30:479–483.Google Scholar
13. Støer, NC, Samuelsen, SO. Inverse probability weighting in nested case-control studies with additional matching—a simulation study. Statist Med 2013;32:5328–5339.Google Scholar
14. Wolkewitz, M, Cooper, BS, Palomar-Martinez, M, Olaechea-Astigarraga, P, Alvarez-Lerma, F, Schumacher, M. Nested case-control studies in cohorts with competing events. Epidemiology 2014;25:122–125.Google Scholar
15. Borgan, Ø, Keogh, R. Nested case–control studies: should one break the matching? Lifetime Data Anal 2015;21:517–541.Google Scholar
16. Salim, A, Yang, Q, Reilly, M. The value of reusing prior nested case-control data in new studies with different outcome. Statist Med 2012;31:1291–1302.Google Scholar
17. Ohneberg, K, Wolkewitz, M, Beyersmann, J, et al. Analysis of clinical cohort data using nested case-control and case-cohort sampling designs. Methods Inf Med 2015;54:505–514.Google Scholar
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