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
Walid El Ansari is a distinguished medical doctor, surgeon, and academic whose work bridges clinical practice and public health research, with a strong focus on obesity, bariatric surgery, and metabolic disorders. He has been actively involved with prominent institutions in Qatar such as Hamad Medical Corporation, Qatar University, and Weill Cornell Medicine-Qatar, where he contributes to both patient care and academic advancement. His research portfolio is extensive, covering topics like long-term outcomes of weight-loss surgery, obesity-related complications, diabetes management, and population health trends. In addition to clinical studies, he has contributed to large-scale systematic reviews and meta-analyses involving vast patient datasets, reflecting a strong emphasis on evidence-based medicine. He has also explored areas such as men’s reproductive and sexual health, lifestyle behaviors, and health education, particularly in the Middle East and North Africa region. With numerous publications in international peer-reviewed journals and collaborations across countries, he is recognized for his multidisciplinary approach that integrates surgery, epidemiology, and health sciences, making a significant impact on both clinical practice and global health research.
