Background: Days alive without life support (DAWOLS) and similar outcomes that seek to summarise mortality and non-mortality experiences are increasingly used in critical care research. The use of these outcomes is challenged by different definitions and non-normal outcome distributions that complicate statistical analysis decisions.
Methods: We scrutinized the central methodological considerations when using DAWOLS and similar outcomes and provide a description and overview of the pros and cons of various statistical methods for analysis supplemented with a comparison of these methods using data from the COVID STEROID 2 randomised clinical trial. We focused on readily available regression models of increasing complexity (linear, hurdle-negative binomial, zero-one-inflated beta, and cumulative logistic regression models) that allow comparison of multiple treatment arms, adjustment for covariates and interaction terms to assess treatment effect heterogeneity.
Results: In general, the simpler models adequately estimated group means despite not fitting the data well enough to mimic the input data. The more complex models better fitted and thus better replicated the input data, although this came with increased complexity and uncertainty of estimates. While the more complex models can model separate components of the outcome distributions (i.e., the probability of having zero DAWOLS), this complexity means that the specification of interpretable priors in a Bayesian setting is difficult. Finally, we present multiple examples of how these outcomes may be visualised to aid assessment and interpretation.
Conclusions: This summary of central methodological considerations when using, defining, and analysing DAWOLS and similar outcomes may help researchers choose the definition and analysis method that best fits their planned studies.
Trial Registration: COVID STEROID 2 trial, ClinicalTrials.gov: NCT04509973, ctri.nic.in: CTRI/2020/10/028731.
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http://dx.doi.org/10.1186/s12874-023-01963-z | DOI Listing |
BMC Med Res Methodol
June 2023
Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Background: Days alive without life support (DAWOLS) and similar outcomes that seek to summarise mortality and non-mortality experiences are increasingly used in critical care research. The use of these outcomes is challenged by different definitions and non-normal outcome distributions that complicate statistical analysis decisions.
Methods: We scrutinized the central methodological considerations when using DAWOLS and similar outcomes and provide a description and overview of the pros and cons of various statistical methods for analysis supplemented with a comparison of these methods using data from the COVID STEROID 2 randomised clinical trial.
Acta Anaesthesiol Scand
July 2023
Department of Intensive Care, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark.
Background: Trials in critically ill patients increasingly focus on days alive without life support (DAWOLS) or days alive out of hospital (DAOOH) and health-related quality of life (HRQoL). DAWOLS and DAOOH convey more information than mortality and are simpler and faster to collect than HRQoL. However, whether these outcomes are associated with HRQoL is uncertain.
View Article and Find Full Text PDFActa Anaesthesiol Scand
February 2022
Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
Background: Mortality is often the primary outcome in randomised clinical trials (RCTs) conducted in critically ill patients. Due to increased awareness on survivors after critical illness and outcomes other than mortality, health-related quality of life (HRQoL) and days alive without life support (DAWOLS) or days alive and out of hospital (DAAOOH) are increasingly being used. DAWOLS and DAAOOH convey more information than mortality, are easier to collect than HRQoL, and are usually assessed at earlier time points, which may be preferable in some situations.
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