Georgina Imberger1, Christian Gluud, John Boylan, Jørn Wetterslev. 1. From the *Copenhagen Trial Unit, Centre for Clinical Intervention Research, Rigshospitalet, Copenhagen, Denmark; †Department of Anesthesia & Perioperative Medicine, Monash University, Melbourne, Australia; and ‡ Department of Anaesthesia, St. Vincent's Hospital, Dublin, Ireland.
Abstract
BACKGROUND: The GRADE Working Group assessment of the quality of evidence is being used increasingly to inform clinical decisions and guidelines. The assessment involves explicit consideration of all sources of uncertainty. One of these sources is imprecision or random error. Many published meta-analyses are underpowered and likely to be updated in the future. When data are sparse and there are repeated updates, the risk of random error is increased. Trial Sequential Analysis (TSA) is one of several methodologies that estimates this increased risk (and decreased precision) in meta-analyses. With nominally statistically significant meta-analyses of anesthesiologic interventions, we used TSA to estimate power and imprecision in the context of sparse data and repeated updates. METHODS: We conducted a search to identify all systematic reviews with meta-analyses that investigated an intervention that may be implemented by an anesthesiologist during the perioperative period. We randomly selected 50 meta-analyses that reported a statistically significant dichotomous outcome in their abstract. We applied TSA to these meta-analyses by using 2 main TSA approaches: relative risk reduction 20% and relative risk reduction consistent with the conventional 95% confidence limit closest to null. We calculated the power achieved by each included meta-analysis, by using each TSA approach, and we calculated the proportion that maintained statistical significance when allowing for sparse data and repeated updates. RESULTS: From 11,870 titles, we found 682 systematic reviews that investigated anesthesiologic interventions. In the 50 sampled meta-analyses, the median number of trials included was 8 (interquartile range [IQR], 5-14), the median number of participants was 964 (IQR, 523-1736), and the median number of participants with the outcome was 202 (IQR, 96-443). By using both of our main TSA approaches, only 12% (95% CI, 5%-25%) of the meta-analyses had power ≥ 80%, and only 32% (95% CI, 20%-47%) of the meta-analyses preserved the risk of type 1 error <5%. CONCLUSIONS: Most nominally statistically significant meta-analyses of anesthesiologic interventions are underpowered, and many do not maintain their risk of type 1 error <5% if TSA monitoring boundaries are applied. Consideration of the effect of sparse data and repeated updates is needed when assessing the imprecision of meta-analyses of anesthesiologic interventions.
BACKGROUND: The GRADE Working Group assessment of the quality of evidence is being used increasingly to inform clinical decisions and guidelines. The assessment involves explicit consideration of all sources of uncertainty. One of these sources is imprecision or random error. Many published meta-analyses are underpowered and likely to be updated in the future. When data are sparse and there are repeated updates, the risk of random error is increased. Trial Sequential Analysis (TSA) is one of several methodologies that estimates this increased risk (and decreased precision) in meta-analyses. With nominally statistically significant meta-analyses of anesthesiologic interventions, we used TSA to estimate power and imprecision in the context of sparse data and repeated updates. METHODS: We conducted a search to identify all systematic reviews with meta-analyses that investigated an intervention that may be implemented by an anesthesiologist during the perioperative period. We randomly selected 50 meta-analyses that reported a statistically significant dichotomous outcome in their abstract. We applied TSA to these meta-analyses by using 2 main TSA approaches: relative risk reduction 20% and relative risk reduction consistent with the conventional 95% confidence limit closest to null. We calculated the power achieved by each included meta-analysis, by using each TSA approach, and we calculated the proportion that maintained statistical significance when allowing for sparse data and repeated updates. RESULTS: From 11,870 titles, we found 682 systematic reviews that investigated anesthesiologic interventions. In the 50 sampled meta-analyses, the median number of trials included was 8 (interquartile range [IQR], 5-14), the median number of participants was 964 (IQR, 523-1736), and the median number of participants with the outcome was 202 (IQR, 96-443). By using both of our main TSA approaches, only 12% (95% CI, 5%-25%) of the meta-analyses had power ≥ 80%, and only 32% (95% CI, 20%-47%) of the meta-analyses preserved the risk of type 1 error <5%. CONCLUSIONS: Most nominally statistically significant meta-analyses of anesthesiologic interventions are underpowered, and many do not maintain their risk of type 1 error <5% if TSA monitoring boundaries are applied. Consideration of the effect of sparse data and repeated updates is needed when assessing the imprecision of meta-analyses of anesthesiologic interventions.
Authors: Sofie L Rygård; Andreas B Jonsson; Martin B Madsen; Anders Perner; Lars B Holst; Pär I Johansson; Jørn Wetterslev Journal: Intensive Care Med Date: 2018-01-25 Impact factor: 17.440
Authors: Marija Barbateskovic; Søren Marker; Anders Granholm; Carl Thomas Anthon; Mette Krag; Janus Christian Jakobsen; Anders Perner; Jørn Wetterslev; Morten Hylander Møller Journal: Intensive Care Med Date: 2019-01-24 Impact factor: 17.440
Authors: Marija Barbateskovic; Olav L Schjørring; Sara Russo Krauss; Janus C Jakobsen; Christian S Meyhoff; Rikke M Dahl; Bodil S Rasmussen; Anders Perner; Jørn Wetterslev Journal: Cochrane Database Syst Rev Date: 2019-11-27
Authors: Geert Koster; Hanneke J Bekema; Jørn Wetterslev; Christian Gluud; Frederik Keus; Iwan C C van der Horst Journal: Intensive Care Med Date: 2016-07-22 Impact factor: 17.440