Abstract

Objectives: We aimed to highlight the effects of entering duplicated or overlapping data from different published studies that use the same data registries into a meta-analysis, including its identification and management using a novel structured framework.

Study Design and Setting: Secondary analysis of data from a proportional meta-analysis of 30-day cumulative incidence of venous thromboembolic events (VTE) after metabolic and bariatric surgery was performed. Sensitivity analysis was conducted: a) including all studies regardless of duplication (uncorrected sample) and b) comparing it to a corrected sample of studies. We developed a decision tree framework to identify duplicated data from prospective studies and data registries.

Results: Biasing from duplicated data, primarily from data registries, underestimated the incidence of VTE in the literature by 0.15% of the patient population (an erroneous difference equivalent to 22.06% of total VTE). This error persisted at 8.16% of total VTE when limiting to studies using a primarily laparoscopic approach. The decision tree framework used a comparison of the data source (country and hospital or registry), sampling time frame (dates/years of included data) and inclusion characteristics (included procedures/diagnoses or inclusion criteria) to identify potentially duplicated data. Inter-rater reliability was excellent (k =1.00, P <0.001), although only 17.86% of studies coded as containing data duplication were verified by the authors while the remaining studies could not be verified. Lastly, we identified a strong lack of diversity in the geographical origins of the data from the included studies.

Conclusion: Inadvertently including duplicated data in a meta-analysis can result in substantially inaccurate pooled estimates. We outline a comprehensive decision tree framework that future researchers can apply to assist with decision making when identifying and managing duplicated data, including that from prospective trials and data registries or other publicly accessible datasets.

Biography

Prof Dr Walid El Ansari is Professor of Public Health Medicine and Clinical Population Sciences at College of Medicine, Ajman University, United Arab Emirates. Prior to that, he was for 10 years, a senior consultant in the Dept. of Surgery, and the Clinical Research Lead across all surgical specialties at Hamad Medical Corporation hospitals, Qatar. He was also a full Professor at the College of Medicine, Qatar University; Weill Cornell Medicine in Qatar and New York; Skovde University in Sweden; University of East London and Newham University Hospital, London, and a visiting professor at the University of Southern Denmark. Dr El Ansari has held posts in Egypt, South Africa, UK, USA, Austria, Qatar, UAE, Denmark and Sweden. He sits on the editorial board of many international prestigious PubMed-cited journals, has been associate editor and guest editor for several, and is a valued reviewer for a multitude of international journals. Walid has undertaken commissioned work for the WHO headquarters in Geneva, Switzerland, The European Parliament and The European Court of Auditors in Luxembourg, and the World Bank and University of Cambridge, UK. He was invited speaker at the Institute of Medicine – USA, a visiting scholar at UCLA, and invited member to The European Centre for Disease Prevention and Control (ECDC), and to WHO’s Network of Innovators Geneva. Prof El Ansari secured several million dollars from prestigious research funding bodies and is widely published, with over 350 publications. Walid is in Stanford University’s list of the top 2% of scientists worldwide. He is widely published on obesity and Metabolic and Bariatric Surgery, and his work was selected as one of the best 5 papers in Metabolic and Bariatric surgery from the Middle East and North Africa.