• James P A McCullough, Jeffrey Lipman, and Jeffrey J Presneill.
• Department of Intensive Care, Gold Coast University Hospital, Gold Coast, QLD, Australia.
• Crit. Care Med. 2018 Dec 1; 46 (12): 1985-1990.

ObjectivesIncomplete biostatistical knowledge among clinicians is widely described. This study aimed to categorize and summarize the statistical methodology within recent critical care randomized controlled trials.DesignDescriptive analysis, with comparison of findings to previous work.SettingTen high-impact clinical journals publishing trials in critical illness.SubjectsRandomized controlled trials published between 2011 and 2015 inclusive.InterventionsData extraction from published reports.Measurements And Main ResultsThe frequency and overall proportion of each statistical method encountered, grouped according to those used to generate each trial's primary outcome and separately according to underlying statistical methodology. Subsequent analysis compared these proportions with previously published reports. A total of 580 statistical tests or methods were identified within 116 original randomized controlled trials published between 2011 and 2015. Overall, the chi-square test was the most commonly encountered (70/116; 60%), followed by the Cox proportional hazards model (63/116; 54%) and logistic regression (53/116; 46%). When classified according to underlying statistical assumptions, the most common types of analyses were tests of 2 × 2 contingency tables and nonparametric tests of rank order. A greater proportion of more complex methodology was observed compared with trial reports from previous work.ConclusionsPhysicians assessing recent randomized controlled trials in critical illness encounter results derived from a substantial and potentially expanding range of biostatistical methods. In-depth training in the assumptions and limitations of these current and emerging biostatistical methods may not be practically achievable for most clinicians, making accessible specialist biostatistical support an asset to evidence-based clinical practice.

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