Connecting a disconnected trial network with a new trial: optimizing the estimation of a comparative effect in a network meta‑analysis

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McKeen, Lauren
Morris, Paul
Morris, Max D.
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Springer Nature
O'Connor, Annette
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The Department of Statistics seeks to teach students in the theory and methodology of statistics and statistical analysis, preparing its students for entry-level work in business, industry, commerce, government, or academia.

The Department of Statistics was formed in 1948, emerging from the functions performed at the Statistics Laboratory. Originally included in the College of Sciences and Humanities, in 1971 it became co-directed with the College of Agriculture.

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Veterinary Diagnostic and Production Animal Medicine
The mission of VDPAM is to educate current and future food animal veterinarians, population medicine scientists and stakeholders by increasing our understanding of issues that impact the health, productivity and well-being of food and fiber producing animals; developing innovative solutions for animal health and food safety; and providing the highest quality, most comprehensive clinical practice and diagnostic services. Our department is made up of highly trained specialists who span a wide range of veterinary disciplines and species interests. We have faculty of all ranks with expertise in diagnostics, medicine, surgery, pathology, microbiology, epidemiology, public health, and production medicine. Most have earned certification from specialty boards. Dozens of additional scientists and laboratory technicians support the research and service components of our department.
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Background In network meta-analysis, estimation of a comparative effect can be performed for treatments that are connected either directly or indirectly. However, disconnected trial networks may arise, which poses a challenge to comparing all available treatments of interest. Several modeling approaches attempt to compare treatments from disconnected networks but not without strong assumptions and limitations. Conducting a new trial to connect a disconnected network can enable calculation of all treatment comparisons and help researchers maximize the value of the existing networks. Here, we develop an approach to finding the best connecting trial given a specific comparison of interest. Methods We present formulas to quantify the variation in the estimation of a particular comparative effect of interest for any possible connecting two-arm trial. We propose a procedure to identify the optimal connecting trial that minimizes this variation in effect estimation. Results We show that connecting two treatments indirectly might be preferred to direct connection through a new trial, by leveraging information from the existing disconnected networks. Using a real network of studies on the use of vaccines in the treatment of bovine respiratory disease (BRD), we illustrate a procedure to identify the best connecting trial and confirm our findings via simulation. Conclusion Researchers wishing to conduct a connecting two-arm study can use the procedure provided here to identify the best connecting trial. The choice of trial that minimizes the variance of a comparison of interest is network dependent and it is possible that connecting treatments indirectly may be preferred to direct connection.
This article is published as McKeen, Lauren, Paul Morris, Chong Wang, Max D. Morris, and Annette M. O’Connor. "Connecting a disconnected trial network with a new trial: optimizing the estimation of a comparative effect in a network meta-analysis." BMC Medical Research Methodology 23, no. 1 (2023): 1-13. DOI: 10.1186/s12874-023-01896-7. Copyright 2023 The Author(s). Attribution 4.0 International (CC BY 4.0). Posted with permission.
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