Using information from network meta-analyses to optimize the power and sample allocation of a subsequent trial with a new treatment Hu, Dapeng Wang, Chong Ye, Fangshu O'Connor, Annette
dc.contributor.department Veterinary Diagnostic and Production Animal Medicine
dc.contributor.department Statistics 2023-04-17T20:56:59Z 2023-04-17T20:56:59Z 2022-11-22
dc.description.abstract 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.
dc.description.comments 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.
dc.language.iso en
dc.publisher Springer Nature
dc.source.uri *
dc.subject.disciplines DegreeDisciplines::Physical Sciences and Mathematics::Statistics and Probability::Statistical Methodology
dc.subject.keywords Network meta-analysis
dc.subject.keywords Sample size
dc.subject.keywords Clinical trial design
dc.subject.keywords Evidence synthesis
dc.title Using information from network meta-analyses to optimize the power and sample allocation of a subsequent trial with a new treatment
dc.type Article
dspace.entity.type Publication
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