Fragility of results from randomized controlled trials supporting the guidelines for the treatment of osteoporosis: a retrospective analysis

This is the first report on the fragility of results from randomized controlled trials (RCTs) for the treatment of osteoporosis. The results of aforementioned RCTs appear to depend on a small number of events and are generally statistically fragile.IntroductionOsteoporosis remains a health concern worldwide. Evidence-based guideline recommendations that are mainly based on results of clinical trials are important to clinical decision-making. The fragility index (FI) is a novel statistical metric to measure the fragility of results from an RCT. Our study aimed to analyze the fragility of the clinical trials referenced in the guidelines for the treatment of osteoporosis.MethodsTrials were included if they investigated primary osteoporosis, randomized patients to treatment or control in a 1:1 design, and reported fracture outcome as the primary endpoint. The FI and fragility quotient (FQ) were calculated for assessing the robustness of results from the eligible RCTs. An FI was defined as the minimum number of events in the intervention group that needs to change from a non-event to an event in order to render a significant result non-significant (or vice versa). The FQ was calculated by dividing the FI by the sample size of the trial.ResultsOf the 372 RCTs identified from the guidelines, 42 were eligible for analyses. Their median FI was 10 (25th–75th percentile [Q1-Q3]: 4–18), with a median FQ of 0.007 (Q1-Q3: 0.0017–0.019). Approximately one third of the RCTs had a FI of less than or equal to 5. There were 17 (40.5%) trials where the number of patients lost to follow-up was greater than the FI. The FI was significantly associated with sample size, journal impact factor, and the percent of patients lost to follow-up.ConclusionResults from some RCTs supporting guideline recommendations for the treatment of osteoporosis depend on a small number of events. The FI and FQ may provide additional, intuitive metrics to help interpret the robustness of trial results.