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Cited by 139Pneumonia and Respiratory InfectionsStatistical Methods in EpidemiologyStatistical Methods in Clinical Trials

How to handle mortality when investigating length of hospital stay and time to clinical stability

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Brock, Guy N, Barnes, Christopher, Ramirez, Julio A, Myers, John·2011·BMC Medical Research Methodology
DOI 10.1186/1471-2288-11-144PMC PMC3269825
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Abstract

BACKGROUND: Hospital length of stay (LOS) and time for a patient to reach clinical stability (TCS) have increasingly become important outcomes when investigating ways in which to combat Community Acquired Pneumonia (CAP). Difficulties arise when deciding how to handle in-hospital mortality. Ad-hoc approaches that are commonly used to handle time to event outcomes with mortality can give disparate results and provide conflicting conclusions based on the same data. To ensure compatibility among studies investigating these outcomes, this type of data should be handled in a consistent and appropriate fashion. METHODS: Using both simulated data and data from the international Community Acquired Pneumonia Organization (CAPO) database, we evaluate two ad-hoc approaches for handling mortality when estimating the probability of hospital discharge and clinical stability: 1) restricting analysis to those patients who lived, and 2) assigning individuals who die the "worst" outcome (right-censoring them at the longest recorded LOS or TCS). Estimated probability distributions based on these approaches are compared with right-censoring the individuals who died at time of death (the complement of the Kaplan-Meier (KM) estimator), and treating death as a competing risk (the cumulative incidence estimator). Tests for differences in probability distributions based on the four methods are also contrasted. RESULTS: The two ad-hoc approaches give different estimates of the probability of discharge and clinical stability. Analysis restricted to patients who survived is conceptually problematic, as estimation is conditioned on events that happen at a future time. Estimation based on assigning those patients who died the worst outcome (longest LOS and TCS) coincides with the complement of the KM estimator based on the subdistribution hazard, which has been previously shown to be equivalent to the cumulative incidence estimator. However, in either case the time to in-hospital mortality is ignored, preventing simultaneous assessment of patient mortality in addition to LOS and/or TCS. The power to detect differences in underlying hazards of discharge between patient populations differs for test statistics based on the four approaches, and depends on the underlying hazard ratio of mortality between the patient groups. CONCLUSIONS: Treating death as a competing risk gives estimators which address the clinical questions of interest, and allows for simultaneous modelling of both in-hospital mortality and TCS / LOS. This article advocates treating mortality as a competing risk when investigating other time related outcomes.

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  • Multivariable models were used to compare periods, adjusting for relevant patient characteristics showing a significant difference between periods at p<0.20.

    “Multivariable models were used to compare periods, adjusting for relevant patient characteristics.”

    From Lab efficiency and improvements in the management of bloodstream infections: a before and after study (2025) · cites this paper as [5]

    The Discussion states that the authors did not address incorporating additional risk factors through regression models. The Methods section instead describes tests comparing patient groups, not multivariable models adjusting for patient characteristics. No page numbers are available in our copy. (LLM, conf=0.88)

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Authors
Brock, G. N., Barnes, C., Ramirez, J. A., & Myers, J.
Affiliations
University of Louisville (US)
Corresponding author
  • Brock, Guy N
  • Myers, John
ORCID
Venue
BMC Medical Research Methodology
Publisher
Springer Science and Business Media LLC
Published
2011-10-26
Volume
11
Issue
1
Pages
144-144
ISSN
1471-2288 (ISSN-L)
DOI
10.1186/1471-2288-11-144
PMC
PMC3269825
OpenAlex
W1965493147
Cited by
138
References
58
Citations by year
2026: 6 · 2025: 10 · 2024: 12 · 2023: 10 · 2022: 24 · 2021: 19 · 2020: 17 · 2019: 8 · 2018: 8 · 2017: 13 · 2016: 5 · 2015: 5
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Research Methodology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health (ES014443)
  • U.S. Public Health Service (P02 728 0800015499, RSGHP-09-099-01)
  • National Institute of Environmental Health Sciences (P30ES014443)
  • National Institute of Dental and Craniofacial Research (P20-RR/DE177702)
  • National Institute for Occupational Safety and Health (1r03oh008957-01a2)
Topics
  • Health Sciences › Medicine › Epidemiology › Pneumonia and Respiratory Infections
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
community-acquired pneumonia, length of hospital stay, time to clinical stability, in-hospital mortality, competing risks, right censoring, hospital discharge, time-to-event outcomes, Kaplan-Meier estimator, statistical power
MeSH terms
Algorithms, Computer Simulation, Follow-Up Studies, Humans, Length of Stay, Patient Discharge, Pneumonia, Risk Factors, Time Factors, Models, Statistical, Treatment Outcome, Hospital Mortality, Community-Acquired Infections, Kaplan-Meier Estimate

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