Sample-size determination and adherence in randomised controlled trials published in anaesthetic journals. (4th March 2018)

• Record Type: Journal Article
• Authors: Nontshe, M; Khan, S; Mandebvu, T; Merrifield, B; Rodseth, RN
• Is Part Of: Southern African journal of anaesthesia and analgesia. Volume 24:Number 2(2018)
• Page Start: 40
• Page End: 46
• Publication Date: 2018-03-04
• Subjects: anaesthesia -- clinical trial -- power calculations -- sample size -- treatment effect -- type II error; Anaesthesia -- Periodicals; Analgesia -- Periodicals; 617.96
• DOI: 10.1080/22201181.2018.1439602
• Languages: English
• ISSNs: 2220-1181

Abstract

Abstract : Background : Sample-size calculations are critical to ensure that randomised control trials return robust and reliable results. The estimated treatment effects used in these calculations is often significantly different from the actual treatment effect and can dramatically impact trial validity. Methods : This study examined sample-size calculations in randomised controlled trials designed to show superiority between two-arm parallel groups with a single primary outcome that were published in the top five anaesthetic journals for 2014 (as per Thomson Reuters impact factors). In particular, it sought to determine treatment effect estimations used in a priori sample-size calculations and compare them with actual treatment effects. Results : A PubMed search identified 209 possible articles; 52 were drawn for full text review; and 28 were included in the final analysis. The relative difference between expected and actual event rates was greater than 20% in 80% of trials and greater than 50% in 44% of trials. Conclusions : Unrealistic assumptions of treatment effects in randomised controlled trials published in anaesthesia journals are common. Trial sample sizes should be calculated thoughtfully and realistically and should be fully reported in both trial protocols and publications. Researchers should be aware of the opportunity cost as well as the possible dangers to patients when unrealistic assumptions are made. Where possible researchers should collaborate to achieve meaningful trial sample sizes to ensure robust clinical findings.