Use of days alive without life support and similar count outcomes in randomised clinical trials – an overview and comparison of methodological choices and analysis methods

BMC Medical Research Methodology

Research

Michael O. Harhay¹  Anders Perner²  Anders Granholm²  Marie Warrer Munch²  Morten Hylander Møller²  Benjamin Skov Kaas-Hansen³  Fernando G. Zampieri⁴  Theis Lange⁵  Aksel Karl Georg Jensen⁵ 

[1] Clinical Trials Methods and Outcomes Lab, Palliative and Advanced Illness Research Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA;Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA;Department of Intensive Care 4131, Copenhagen University Hospital – Rigshospitalet, DK-2100, Copenhagen, Denmark;Department of Intensive Care 4131, Copenhagen University Hospital – Rigshospitalet, DK-2100, Copenhagen, Denmark;Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark;HCor Research Institute, São Paulo, Brazil;Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, Alberta, Canada;Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark;

关键词: Days alive without life support;    Days alive out of hospital;    Count outcomes;    Analysis methods;    Statistical models;   

DOI  :  10.1186/s12874-023-01963-z

 received in 2023-02-15, accepted in 2023-06-03,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundDays 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.MethodsWe 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.ResultsIn 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.ConclusionsThis 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 registrationCOVID STEROID 2 trial, ClinicalTrials.gov: NCT04509973, ctri.nic.in: CTRI/2020/10/028731.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 1

Fig. 1

Fig. 4

41116_2023_37_Article_IEq159.gif