Infection Control & Hospital Epidemiology (2018), 39, 1196–1201 doi:10.1017/ice.2018.186
Original Article
Improving nested case-control studies to conduct a full competing-risks analysis for nosocomial infections
Derek Hazard MSc1, Martin Schumacher PhD1, Mercedes Palomar-Martinez PhD2, Francisco Alvarez-Lerma PhD3, Pedro Olaechea-Astigarraga PhD3 and Martin Wolkewitz PhD1 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany, 2Hospital Universitari Arnau de
Vilanova, Lleida, Universitat Autonoma de Barcelona, Barcelona, Spain and 3Service of Intensive Care Medicine, Parc de Salut Mar, Barcelona, Spain 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.
(Received 12 March 2018; accepted 14 July 2018; electronically published August 30, 2018)
In time-to-event analyses, a competing-risks setting occurs when 1 or several events preclude the observation of an event of interest.1,2 Recent studies have shown that this setting is of special importance when analyzing risk factors for nosocomial infections (NIs).3,4 For instance, a cohort study of hospitalized children in Kenya reported no association between burns and nosocomial bacteremia.5 How- ever, a complete competing-risks analysis would have detected an effect on length of stay, which would have yielded a cumulative risk 3 times higher for children with burns.1, 6 This analysis is achieved by determining the influence of a risk factor on each separate competing event.7 The decreasing effect of a risk factor (eg, burns) on the rate of 1 event (eg, discharge from hospital) can have an increasing effect on the risk of experiencing a competing event (eg, nosocomial bacteremia). Therefore, ignoring competing risks when analyzing hospital-acquired infections can lead to biased results and incomplete conclusions. When the covariate information is expensive or difficult to
acquire, separate nested case-control (NCC) study designs can be used to ascertain the influence of risk factors on NI8,9 and its competing events. An NCC requires the collection of covariate information for cases and time-matched controls that are a subset of the total available controls in the full cohort, thus achieving a
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
Cite this article: Hazard D, et al. (2018). Improving nested case-control studies to
conduct a full competing-risks analysis for nosocomial infections. Infection Control & Hospital Epidemiology 2018, 39, 1196–1201. doi: 10.1017/ice.2018.186
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved.
reduction in time and resources. In traditional practice, controls would be sampled for each competing event and would only be included in the analysis of the competing event for which they were selected. However, Samuelsen10 proposed pooling these controls together and employing a weighted Cox model so that all selected controls are used in the analysis of each competing event. This method led to improvements in precision over keeping the controls separated. In this study, we adapted the methodology of Samuelsen and
applied it to a common setting in hospital epidemiology. Our goal was to estimate the influence of potential risk factors on acquiring an NI and on the competing event of dying or being discharged without infection in 2 Spanish intensive care units (ICUs). By reusing controls from 1 initial sampling, we avoided the added effort of sampling with respect to every competing event (ie, “sample for one, analyze for all”). Furthermore, our extension requires additional data that are routinely collected (and not any additional covariate data), thus enabling a competing-risks rea- nalysis of previously conducted NCC studies. These improve- ments can be achieved with little cost to the researcher.
Methods
Data were collected from 6,563 admissions in 2 Spanish ICUs within the ENVIN-HELICS network. We removed 5 individuals from the original cohort due to missing values. Among the 6,563 patients admitted, 432 (6.58%) acquired an NI (ie, bloodstream
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