{"title":"关于药物相互作用试验的样本量计算。","authors":"Paul Meyvisch, Mitra Ebrahimpoor","doi":"10.1002/pst.2367","DOIUrl":null,"url":null,"abstract":"<p><p>Drug-drug interaction (DDI) trials are an important part of drug development as they provide evidence on the benefits and risks when two or more drugs are taken concomitantly. Sample size calculation is typically recommended to be based on the existence of clinically justified no-effect boundaries but these are challenging to define in practice, while the default no-effect boundaries of 0.8-1.25 are known to be overly conservative requiring a large sample size. In addition, no-effect boundaries are of little use when there is prior pharmacological evidence that a mild or moderate interaction between two drugs may be present, in which case effect boundaries would be more useful. We introduce precision-based sample size calculation that accounts for both the stochastic nature of the pharmacokinetic parameters and the anticipated width of (no-)effect boundaries, should these exist. The methodology is straightforward, requires considerably less sample size and has favorable operating characteristics. A case study on statins is presented to illustrate the ideas.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On sample size calculation in drug interaction trials.\",\"authors\":\"Paul Meyvisch, Mitra Ebrahimpoor\",\"doi\":\"10.1002/pst.2367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Drug-drug interaction (DDI) trials are an important part of drug development as they provide evidence on the benefits and risks when two or more drugs are taken concomitantly. Sample size calculation is typically recommended to be based on the existence of clinically justified no-effect boundaries but these are challenging to define in practice, while the default no-effect boundaries of 0.8-1.25 are known to be overly conservative requiring a large sample size. In addition, no-effect boundaries are of little use when there is prior pharmacological evidence that a mild or moderate interaction between two drugs may be present, in which case effect boundaries would be more useful. We introduce precision-based sample size calculation that accounts for both the stochastic nature of the pharmacokinetic parameters and the anticipated width of (no-)effect boundaries, should these exist. The methodology is straightforward, requires considerably less sample size and has favorable operating characteristics. A case study on statins is presented to illustrate the ideas.</p>\",\"PeriodicalId\":19934,\"journal\":{\"name\":\"Pharmaceutical Statistics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pharmaceutical Statistics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/pst.2367\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/2/14 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmaceutical Statistics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/pst.2367","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/14 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
On sample size calculation in drug interaction trials.
Drug-drug interaction (DDI) trials are an important part of drug development as they provide evidence on the benefits and risks when two or more drugs are taken concomitantly. Sample size calculation is typically recommended to be based on the existence of clinically justified no-effect boundaries but these are challenging to define in practice, while the default no-effect boundaries of 0.8-1.25 are known to be overly conservative requiring a large sample size. In addition, no-effect boundaries are of little use when there is prior pharmacological evidence that a mild or moderate interaction between two drugs may be present, in which case effect boundaries would be more useful. We introduce precision-based sample size calculation that accounts for both the stochastic nature of the pharmacokinetic parameters and the anticipated width of (no-)effect boundaries, should these exist. The methodology is straightforward, requires considerably less sample size and has favorable operating characteristics. A case study on statins is presented to illustrate the ideas.
期刊介绍:
Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics.
The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.