Balancing events, not patients, maximizes power of the logrank test: and other insights on unequal randomization in survival trials
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908369513611264 |
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| author | Yung, Godwin Rufibach, Kaspar Wolbers, Marcel Lin, Ray Liu, Yi |
| author_facet | Yung, Godwin Rufibach, Kaspar Wolbers, Marcel Lin, Ray Liu, Yi |
| contents | We revisit the question of what randomization ratio (RR) maximizes power of the logrank test in event-driven survival trials under proportional hazards (PH). By comparing three approximations of the logrank test (Schoenfeld, Freedman, Rubinstein) to empirical simulations, we find that the RR that maximizes power is the RR that balances number of events across treatment arms at the end of the trial. This contradicts the common misconception implied by Schoenfeld's approximation that 1:1 randomization maximizes power. Besides power, we consider other factors that might influence the choice of RR (accrual, trial duration, sample size, etc.). We perform simulations to better understand how unequal randomization might impact these factors in practice. Altogether, we derive 6 insights to guide statisticians in the design of survival trials considering unequal randomization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03420 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Balancing events, not patients, maximizes power of the logrank test: and other insights on unequal randomization in survival trials Yung, Godwin Rufibach, Kaspar Wolbers, Marcel Lin, Ray Liu, Yi Methodology Applications We revisit the question of what randomization ratio (RR) maximizes power of the logrank test in event-driven survival trials under proportional hazards (PH). By comparing three approximations of the logrank test (Schoenfeld, Freedman, Rubinstein) to empirical simulations, we find that the RR that maximizes power is the RR that balances number of events across treatment arms at the end of the trial. This contradicts the common misconception implied by Schoenfeld's approximation that 1:1 randomization maximizes power. Besides power, we consider other factors that might influence the choice of RR (accrual, trial duration, sample size, etc.). We perform simulations to better understand how unequal randomization might impact these factors in practice. Altogether, we derive 6 insights to guide statisticians in the design of survival trials considering unequal randomization. |
| title | Balancing events, not patients, maximizes power of the logrank test: and other insights on unequal randomization in survival trials |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2407.03420 |