Using information from network meta-analyses to optimize the power and sample allocation of a subsequent trial with a new treatment

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2022-11-22
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Hu, Dapeng
Ye, Fangshu
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Springer Nature
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O'Connor, Annette
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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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Statistics

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.

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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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1948-present

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Background: A critical step in trial design is determining the sample size and sample allocation to ensure the proposed study has sufficient power to test the hypothesis of interest: superiority, equivalence, or non-inferiority. When data are available from prior trials and leveraged with the new trial to answer the scientific questions, the value of society’s investment in prior research is increased. When prior information is available, the trial design including the sample size and allocation should be adapted accordingly, yet the current approach to trial design does not utilize such information. Ensuring we maximize the value of prior research is essential as there are always constraints on resources, either physical or financial, and designing a trial with adequate power can be a challenge. Methods: We propose an approach to increasing the power of a new trial by incorporating evidence from a network meta-analysis into the new trial design and analysis. We illustrate the methodology through an example network meta-analysis, where the goal is to identify the optimal allocation ratio for the new three-arm trial, which involves the reference treatment, the new treatment, and the negative control. The primary goal of the new trial is to show that the new treatment is non-inferior to the reference treatment. It may also be of interest to know if the new treatment is superior to the negative control. We propose an optimal treatment allocation strategy which is derived from minimizing the standard error of the log odds ratio estimate of the comparison of interest. We conducted a simulation study to assess the proposed methods to design a new trial while borrowing information from the existing network meta-analysis and compare it to even allocation methods. Results: Using mathematical derivation and simulations, we document that our proposed approach can borrow information from a network meta-analysis to modify the treatment allocation ratio and increase the power of the new trial given a fixed total sample size or to reduce the total sample size needed to reach a desired power. Conclusions: When prior evidence about the hypotheses of interest is available, the traditional equal allocation strategy is not the most powerful approach anymore. Our proposed methodology can improve the power of trial design, reduce the cost of trials, and maximize the utility of prior investments in research.
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This article is published as Hu, Dapeng, Chong Wang, Fangshu Ye, and Annette M. O’Connor. "Using information from network meta-analyses to optimize the power and sample allocation of a subsequent trial with a new treatment." BMC Medical Research Methodology 22, no. 1 (2022): 299. DOI: 10.1186/s12874-022-01792-6. Copyright 2023 The Author(s). Attribution 4.0 International (CC BY 4.0). Posted with permission.
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