Details

Autor(en) / Beteiligte

• Lee, Yung
• Samarasinghe, Yasith
• Chen, Lucy H
• Hapugall, Akithma
• Javidan, Arshia
• McKechnie, Tyler
• Doumouras, Aristithes
• Hong, Dennis

Titel

Fragility of statistically significant outcomes in randomized trials comparing bariatric surgeries

Ist Teil von

• International Journal of Obesity, 2023-07, Vol.47 (7), p.546-553

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• Randomized controlled trials (RCTs) are regarded as high-level evidence, but the strength of their P values can be difficult to ascertain. The Fragility Index (FI) is a novel metric that evaluates the frailty of trial findings. It is defined as the minimum number of patients required to change from a non-event to event for the findings to lose statistical significance. This study aims to characterize the robustness of bariatric surgery RCTs by examining their FIs. A search was conducted in MEDLINE, EMBASE, and CENTRAL from January 2000 to February 2022 for RCTs comparing two bariatric surgeries with statistically significant dichotomous outcomes. Bivariate correlation was conducted to identify associations between FI and trial characteristics. A total of 35 RCTs were included with a median sample size of 80 patients (Interquartile range [IQR] 58-109). The median FI was 2 (IQR 0-5), indicating that altering the status of two patients in one treatment arm would overturn the statistical significance of results. Subgroup analyses of RCTs evaluating diabetes-related outcomes produced a FI of 4 (IQR 2-6.5), while RCTs comparing Roux-en-Y gastric bypass and sleeve gastrectomy had an FI of 2 (IQR 0.5-5). Increasing FI was found to be correlated with decreasing P value, increasing sample size, increasing number of events, and increasing journal impact factor. Bariatric surgery RCTs are fragile, with only a few patients required to change from non-events to events to reverse the statistical significance of most trials. Future research should examine the use of FI in trial design.