{"title":"MOVER 检验配对设计中两种二元结果治疗之间的差异是否具有非劣效性。","authors":"Liangchang Xiu, Linlin Xie, Haiyi Yan, Chunxin Wu, Huansheng Liu, Chao Chen","doi":"10.1080/10543406.2024.2390888","DOIUrl":null,"url":null,"abstract":"<p><p>A non-inferiority trial is usually conducted to investigate whether a new drug/treatment is no worse than a reference drug/treatment by a small, pre-specified, non-inferiority margin. This study aimed to assess the non-inferiority of the difference between two binary-outcome treatments in a matched-pairs design based on the method of variance of estimates recovery (MOVER). The processes for estimating the confidence interval of a single proportion included in the MOVER are the Wilson score interval, Agresti - Coull interval, Jeffreys interval, modified Jeffreys interval, score method with continuity correction, and arcsin interval. The performance of the six MOVER tests, the fiducial test, and the restricted maximum likelihood estimation test were evaluated by comparing their type I error rates and power at different pre-assigned levels and with varying combinations of parameters. The evaluation results showed that the modified Jeffreys MOVER test can be a competitive alternative to the other recommended tests. It can control type I errors well, and its power is not inferior to other methods. The proposed tests were illustrated with three real-world examples.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-14"},"PeriodicalIF":1.2000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MOVER tests for non-inferiority of the difference between two binary-outcome treatments in the matched-pairs design.\",\"authors\":\"Liangchang Xiu, Linlin Xie, Haiyi Yan, Chunxin Wu, Huansheng Liu, Chao Chen\",\"doi\":\"10.1080/10543406.2024.2390888\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>A non-inferiority trial is usually conducted to investigate whether a new drug/treatment is no worse than a reference drug/treatment by a small, pre-specified, non-inferiority margin. This study aimed to assess the non-inferiority of the difference between two binary-outcome treatments in a matched-pairs design based on the method of variance of estimates recovery (MOVER). The processes for estimating the confidence interval of a single proportion included in the MOVER are the Wilson score interval, Agresti - Coull interval, Jeffreys interval, modified Jeffreys interval, score method with continuity correction, and arcsin interval. The performance of the six MOVER tests, the fiducial test, and the restricted maximum likelihood estimation test were evaluated by comparing their type I error rates and power at different pre-assigned levels and with varying combinations of parameters. The evaluation results showed that the modified Jeffreys MOVER test can be a competitive alternative to the other recommended tests. It can control type I errors well, and its power is not inferior to other methods. The proposed tests were illustrated with three real-world examples.</p>\",\"PeriodicalId\":54870,\"journal\":{\"name\":\"Journal of Biopharmaceutical Statistics\",\"volume\":\" \",\"pages\":\"1-14\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Biopharmaceutical Statistics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/10543406.2024.2390888\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biopharmaceutical Statistics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/10543406.2024.2390888","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
摘要
非劣效性试验通常是为了研究一种新药/治疗方法与参考药物/治疗方法相比,是否有很小的、预先指定的非劣效性差值。本研究旨在根据估计值恢复方差法(MOVER),在配对设计中评估两种二元结果治疗之间差异的非劣效性。MOVER 中包含的估算单一比例置信区间的方法有 Wilson 评分区间、Agresti - Coull 区间、Jeffreys 区间、修正 Jeffreys 区间、带连续性校正的评分法和 arcsin 区间。通过比较不同预设水平和不同参数组合下的 I 类错误率和功率,评估了六种 MOVER 检验、fiducial 检验和受限最大似然估计检验的性能。评估结果表明,修改后的 Jeffreys MOVER 检验可以替代其他推荐检验。它能很好地控制 I 型误差,其功率也不比其他方法差。我们用三个实际案例对所提出的检验方法进行了说明。
MOVER tests for non-inferiority of the difference between two binary-outcome treatments in the matched-pairs design.
A non-inferiority trial is usually conducted to investigate whether a new drug/treatment is no worse than a reference drug/treatment by a small, pre-specified, non-inferiority margin. This study aimed to assess the non-inferiority of the difference between two binary-outcome treatments in a matched-pairs design based on the method of variance of estimates recovery (MOVER). The processes for estimating the confidence interval of a single proportion included in the MOVER are the Wilson score interval, Agresti - Coull interval, Jeffreys interval, modified Jeffreys interval, score method with continuity correction, and arcsin interval. The performance of the six MOVER tests, the fiducial test, and the restricted maximum likelihood estimation test were evaluated by comparing their type I error rates and power at different pre-assigned levels and with varying combinations of parameters. The evaluation results showed that the modified Jeffreys MOVER test can be a competitive alternative to the other recommended tests. It can control type I errors well, and its power is not inferior to other methods. The proposed tests were illustrated with three real-world examples.
期刊介绍:
The Journal of Biopharmaceutical Statistics, a rapid publication journal, discusses quality applications of statistics in biopharmaceutical research and development. Now publishing six times per year, it includes expositions of statistical methodology with immediate applicability to biopharmaceutical research in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers:
Drug, device, and biological research and development;
Drug screening and drug design;
Assessment of pharmacological activity;
Pharmaceutical formulation and scale-up;
Preclinical safety assessment;
Bioavailability, bioequivalence, and pharmacokinetics;
Phase, I, II, and III clinical development including complex innovative designs;
Premarket approval assessment of clinical safety;
Postmarketing surveillance;
Big data and artificial intelligence and applications.