ArticleJournal of biopharmaceutical statistics2020
Two-stage optimal designs based on exact variance for a single-arm trial with survival endpoints.
Article in Journal of biopharmaceutical statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Reducing Central Nervous System-Active Medications to Prevent Falls and Injuries Among Older Adults: A Cluster Randomized Clinical Trial.JAMA network open · 2024Trial
- Predicting Conversion Time from Mild Cognitive Impairment to Dementia with Interval-Censored Models.Journal of Alzheimer's disease : JAD · 2024Article
- Article
- Randomized two-stage optimal design for interval-censored data.Journal of biopharmaceutical statistics · 2022Article
- Bayesian single-arm phase II trial designs with time-to-event endpoints.Pharmaceutical statistics · 2021Article
- Optimal two-stage designs based on restricted mean survival time for a single-arm study.Contemporary clinical trials communications · 2021Article
- Partial correlation coefficient for a study with repeated measurements.Statistics in biopharmaceutical research · 2021Article
- Disease progression from mild cognitive impairment to dementia for patients with Alzheimer's disease or Lewy body pathology.Journal of Alzheimer's disease reportsArticle
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Abstract
Sample size calculation based on normal approximations is often associated with the loss of statistical power for a single-arm trial with a time-to-event endpoint. Recently, Wu (2015) derived the exact variance for the one-sample log-rank test under the alternative and showed that a single-arm one-stage study based on exact variance often has power above the nominal level while the type I error rate is controlled. We extend this approach to a single-arm two-stage design by using exact variances of the one-sample log-rank test for the first stage and the two stages combined. The empirical power of the proposed two-stage optimal designs is often not guaranteed under a two-stage design setting, which could be due to the asymptotic bi-variate normal distribution used to estimate the joint distribution of the test statistics. We adjust the nominal power level in the design search to guarantee the simulated power of the identified optimal design being above the nominal level. The sample size and the study time savings of the proposed two-stage designs are substantial as compared to the one-stage design.
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