Chang Xu1, Luis Furuya-Kanamori2, Liliane Zorzela3, Lifeng Lin4, Sunita Vohra5. 1. Department of Population Medicine, College of Medicine, Qatar University, Doha, Qatar. Electronic address: xuchang2016@runbox.com. 2. Research School of Population Health, College of Health and Medicine, Australian National University, Canberra, Australia. 3. Department of Pediatrics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada. 4. Department of Statistics, Florida State University, Tallahassee, FL, USA. 5. Department of Pediatrics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada; Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada.
Abstract
OBJECTIVE: In evidence synthesis practice, researchers often face the problem of how to deal with zero-events. Inappropriately dealing with zero-events studies may lead to research waste and mislead healthcare practice. We propose a framework to guide researchers to better deal with zero-events in meta-analysis. STUDY DESIGN AND SETTING: We used two dimensions, one with respect to the total events count across all studies in the comparative arms in a meta-analysis, and a second with respect to whether included studies have single or both arms with zero-events, to establish the framework for the classification of meta-analysis with zero-events studies. A dataset from Cochrane systematic reviews was used to evaluate the classification. RESULTS: The proposed framework classifies meta-analysis with zero-events studies into six subtypes. The classification matched well to the large real-world dataset. The applicability of existing methods for zero-events were then presented under each meta-analysis subtype based on this framework, with a 5-step principle to help researchers in evidence synthesis practice. CONCLUSIONS: The proposed framework should be considered by researchers when making decisions on the selection of the synthesis methods in a meta-analysis. It also provides a reasonable basis for the development of methodological guidelines to deal with zero-events in meta-analysis.
OBJECTIVE: In evidence synthesis practice, researchers often face the problem of how to deal with zero-events. Inappropriately dealing with zero-events studies may lead to research waste and mislead healthcare practice. We propose a framework to guide researchers to better deal with zero-events in meta-analysis. STUDY DESIGN AND SETTING: We used two dimensions, one with respect to the total events count across all studies in the comparative arms in a meta-analysis, and a second with respect to whether included studies have single or both arms with zero-events, to establish the framework for the classification of meta-analysis with zero-events studies. A dataset from Cochrane systematic reviews was used to evaluate the classification. RESULTS: The proposed framework classifies meta-analysis with zero-events studies into six subtypes. The classification matched well to the large real-world dataset. The applicability of existing methods for zero-events were then presented under each meta-analysis subtype based on this framework, with a 5-step principle to help researchers in evidence synthesis practice. CONCLUSIONS: The proposed framework should be considered by researchers when making decisions on the selection of the synthesis methods in a meta-analysis. It also provides a reasonable basis for the development of methodological guidelines to deal with zero-events in meta-analysis.