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Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network
Fangshu Ye1, Chong Wang2,3, Annette M O'Connor4,5
1Department of Statistics, College of Liberal Arts and Sciences, Iowa State University, Ames, IA, USA.
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 [Formula: see text]. 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 [Formula: see text].
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 [Formula: see text]. 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.
Insights
Designing clinical trials comparing a new treatment to an existing one can be optimized. Incorporating existing network meta-analysis data can enhance power, making indirect or three-arm trials more effective than direct two-arm trials under specific conditions.
Area of Science:
- Clinical Trial Design
- Network Meta-Analysis
- Statistical Power
Background:
- Leveraging existing network meta-analysis data can optimize new clinical trial designs.
- The impact of network meta-analysis on trials comparing an existing treatment (old) to a novel one (new) is under-explored.
- This study investigates optimal trial designs for comparing an old treatment A with a new treatment Z.
Purpose of the Study:
- To identify the most powerful trial design for comparing treatment A (old) and treatment Z (new) given a fixed sample size.
- To evaluate three designs: direct two-arm (A vs. Z), indirect two-arm (B vs. Z), and three-arm (A, B, Z).
Main Methods:
- Comparison of standard errors for estimating effect size between treatments A and Z across the three designs.
- Formulas used for continuous and binary outcomes.
- Simulation studies conducted to assess power under specific conditions.
Main Results:
- For continuous outcomes, the direct two-arm trial (A vs. Z) is consistently most powerful.
- For binary outcomes, power comparisons are conclusive only under specific conditions ([Formula: see text]).
- Simulation results show indirect two-arm and three-arm trials can outperform direct two-arm trials when [Formula: see text].
Conclusions:
- The standard direct two-arm trial is not always the most powerful design.
- Incorporating an additional existing treatment (B) via indirect or three-arm trials can increase statistical power.
- Optimal design choice depends on factors like treatment risks, sample size, and existing network data precision.
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