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The Statistical Curriculum Within Randomized Controlled Trials in Critical Illness*

McCullough, James P. A., MMed(ClinEpi)1,2; Lipman, Jeffrey, MD2–,6; Presneill, Jeffrey J., PhD7,8

doi: 10.1097/CCM.0000000000003380
Methods and Analysis
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Objectives: Incomplete biostatistical knowledge among clinicians is widely described. This study aimed to categorize and summarize the statistical methodology within recent critical care randomized controlled trials.

Design: Descriptive analysis, with comparison of findings to previous work.

Setting: Ten high-impact clinical journals publishing trials in critical illness.

Subjects: Randomized controlled trials published between 2011 and 2015 inclusive.

Interventions: Data extraction from published reports.

Measurements and Main Results: The 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.

Conclusions: Physicians 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.

1Department of Intensive Care, Gold Coast University Hospital, Gold Coast, QLD, Australia.

2University of Queensland, Brisbane, QLD, Australia.

3Department of Intensive Care Services, The Royal Brisbane and Women’s Hospital, Brisbane, QLD, Australia.

4Anaesthesiology and Critical Care, University of Queensland, Brisbane, QLD, Australia.

5Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia.

6The George Institute for Global Health, University of Sydney, Sydney, NSW, Australia.

7Intensive Care Unit, The Royal Melbourne Hospital, Melbourne, VIC, Australia.

8University of Melbourne and Monash University, Melbourne, VIC, Australia.

*See also p. 2052.

This work was performed while the first author was employed at the ICU of the Royal Brisbane and Women’s Hospital, Brisbane, QLD, Australia.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccmjournal).

The authors have disclosed that they do not have any potential conflicts of interest.

For information regarding this article, E-mail: jeffrey.presneill@mh.org.au; intensive@fastmail.com.au

Copyright © by 2018 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.