Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatment
Abstract Background Planning the design of a new trial comparing two treatments already in a network of trials with an a-priori plan to estimate the effect size using a network meta-analysis increases power or reduces the sample size requirements. However, when the comparison of interest is between...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12874-023-02089-y |
| _version_ | 1827764356059758592 |
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| author | Fangshu Ye Chong Wang Annette M. O’Connor |
| author_facet | Fangshu Ye Chong Wang Annette M. O’Connor |
| author_sort | Fangshu Ye |
| collection | DOAJ |
| description | Abstract Background Planning the design of a new trial comparing two treatments already in a network of trials with an a-priori plan to estimate the effect size using a network meta-analysis increases power or reduces the sample size requirements. However, when the comparison of interest is between a treatment already in the existing network (old treatment) and a treatment that hasn’t been studied previously (new treatment), the impact of leveraging information from the existing network to inform trial design has not been extensively investigated. We aim to identify the most powerful trial design for a comparison of interest between an old treatment A and a new treatment Z, given a fixed total sample size. We consider three possible designs: a two-arm trial between A and Z (’direct two-arm’), a two-arm trial between another old treatment B and Z (’indirect two-arm’), and a three-arm trial among A, B, and Z. Methods We compare the standard error of the estimated effect size between treatments A and Z for each of the three trial designs using formulas. For continuous outcomes, the direct two-arm trial always has the largest power, while for a binary outcome, the minimum variances among the three trial designs are conclusive only when $$p_A(1-p_A) \ge p_B(1-p_B)$$ p A ( 1 - p A ) ≥ p B ( 1 - p B ) . Simulation studies are conducted to demonstrate the potential for the indirect two-arm and three-arm trials to outperform the direct two-arm trial in terms of power under the condition of $$p_A(1-p_A) < p_B(1-p_B)$$ p A ( 1 - p A ) < p B ( 1 - p B ) . Results Based on the simulation results, we observe that the indirect two-arm and three-arm trials have the potential to be more powerful than a direct two-arm trial only when $$p_A(1-p_A) < p_B(1-p_B)$$ p A ( 1 - p A ) < p B ( 1 - p B ) . This power advantage is influenced by various factors, including the risk of the three treatments, the total sample size, and the standard error of the estimated effect size from the existing network meta-analysis. Conclusions The standard two-arm trial design between two treatments in the comparison of interest may not always be the most powerful design. Utilizing information from the existing network meta-analysis, incorporating an additional old treatment into the trial design through an indirect two-arm trial or a three-arm trial can increase power. |
| first_indexed | 2024-03-11T11:03:14Z |
| format | Article |
| id | doaj.art-693ee24c63314549a8aeb66e4b148d80 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-03-11T11:03:14Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-693ee24c63314549a8aeb66e4b148d802023-11-12T12:21:31ZengBMCBMC Medical Research Methodology1471-22882023-11-0123111210.1186/s12874-023-02089-yOptimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatmentFangshu Ye0Chong Wang1Annette M. O’Connor2Department of Statistics, College of Liberal Arts and Sciences, Iowa State UniversityDepartment of Statistics, College of Liberal Arts and Sciences, Iowa State UniversityDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State UniversityAbstract Background Planning the design of a new trial comparing two treatments already in a network of trials with an a-priori plan to estimate the effect size using a network meta-analysis increases power or reduces the sample size requirements. However, when the comparison of interest is between a treatment already in the existing network (old treatment) and a treatment that hasn’t been studied previously (new treatment), the impact of leveraging information from the existing network to inform trial design has not been extensively investigated. We aim to identify the most powerful trial design for a comparison of interest between an old treatment A and a new treatment Z, given a fixed total sample size. We consider three possible designs: a two-arm trial between A and Z (’direct two-arm’), a two-arm trial between another old treatment B and Z (’indirect two-arm’), and a three-arm trial among A, B, and Z. Methods We compare the standard error of the estimated effect size between treatments A and Z for each of the three trial designs using formulas. For continuous outcomes, the direct two-arm trial always has the largest power, while for a binary outcome, the minimum variances among the three trial designs are conclusive only when $$p_A(1-p_A) \ge p_B(1-p_B)$$ p A ( 1 - p A ) ≥ p B ( 1 - p B ) . Simulation studies are conducted to demonstrate the potential for the indirect two-arm and three-arm trials to outperform the direct two-arm trial in terms of power under the condition of $$p_A(1-p_A) < p_B(1-p_B)$$ p A ( 1 - p A ) < p B ( 1 - p B ) . Results Based on the simulation results, we observe that the indirect two-arm and three-arm trials have the potential to be more powerful than a direct two-arm trial only when $$p_A(1-p_A) < p_B(1-p_B)$$ p A ( 1 - p A ) < p B ( 1 - p B ) . This power advantage is influenced by various factors, including the risk of the three treatments, the total sample size, and the standard error of the estimated effect size from the existing network meta-analysis. Conclusions The standard two-arm trial design between two treatments in the comparison of interest may not always be the most powerful design. Utilizing information from the existing network meta-analysis, incorporating an additional old treatment into the trial design through an indirect two-arm trial or a three-arm trial can increase power.https://doi.org/10.1186/s12874-023-02089-yNetwork meta-analysisClinical trial designEvidence synthesis |
| spellingShingle | Fangshu Ye Chong Wang Annette M. O’Connor Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatment BMC Medical Research Methodology Network meta-analysis Clinical trial design Evidence synthesis |
| title | Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatment |
| title_full | Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatment |
| title_fullStr | Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatment |
| title_full_unstemmed | Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatment |
| title_short | Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatment |
| title_sort | optimal trial design selection a comparative analysis between two arm and three arm trials incorporating network meta analysis for evaluating a new treatment |
| topic | Network meta-analysis Clinical trial design Evidence synthesis |
| url | https://doi.org/10.1186/s12874-023-02089-y |
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