{"title":"使用伽马分布的单臂临床试验中时间到事件终点的样本量考虑因素","authors":"Junqiang Dai, Jianghua He, Milind A. Phadnis","doi":"10.1016/j.conctc.2024.101344","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Time-to-event (TTE) endpoints are evaluated as the primary endpoint in single-arm clinical trials; however, limited options are available in statistical software for sample size calculation. In single-arm trials with TTE endpoints, the non-parametric log-rank test is commonly used. Parametric options for single-arm design assume survival times follow exponential distribution or Weibull distribution.</p></div><div><h3>Methods</h3><p>The exponential- or Weibull-distributed survival time assumption does not always reflect hazard pattern of real-life diseases. We therefore propose gamma distribution as an alternative parametric option for designing single-arm studies with TTE endpoints. We outline a sample size calculation approach using gamma distribution with a known shape parameter and explain how to extract the gamma shape estimate from previously published resources. In addition, we conduct simulations to assess the accuracy of the extracted gamma shape parameter and to explore the impact on sample size calculation when survival time distribution is misspecified.</p></div><div><h3>Results</h3><p>Our simulations show that if a previously published study (sample sizes <span><math><mrow><mo>≥</mo></mrow></math></span> 60 and censoring proportions <span><math><mrow><mo>≤</mo></mrow></math></span> 20 %) reported median and inter-quartile range of survival time, we can obtain a reasonably accurate gamma shape estimate, and use it to design new studies. When true survival time is Weibull-distributed, sample size calculation could be underestimated or overestimated depending on the hazard shape.</p></div><div><h3>Conclusions</h3><p>We show how to use gamma distribution in designing a single-arm trial, thereby offering more options beyond the exponential and Weibull. We provide a simulation-based assessment to ensure an accurate estimation of the gamma shape and recommend caution to avoid misspecification of the underlying distribution.</p></div>","PeriodicalId":37937,"journal":{"name":"Contemporary Clinical Trials Communications","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2451865424000917/pdfft?md5=798a1b4fa602adf63ab3debcbf80ce33&pid=1-s2.0-S2451865424000917-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Sample size considerations for single-arm clinical trials with time-to-event endpoint using the gamma distribution\",\"authors\":\"Junqiang Dai, Jianghua He, Milind A. Phadnis\",\"doi\":\"10.1016/j.conctc.2024.101344\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Time-to-event (TTE) endpoints are evaluated as the primary endpoint in single-arm clinical trials; however, limited options are available in statistical software for sample size calculation. In single-arm trials with TTE endpoints, the non-parametric log-rank test is commonly used. Parametric options for single-arm design assume survival times follow exponential distribution or Weibull distribution.</p></div><div><h3>Methods</h3><p>The exponential- or Weibull-distributed survival time assumption does not always reflect hazard pattern of real-life diseases. We therefore propose gamma distribution as an alternative parametric option for designing single-arm studies with TTE endpoints. We outline a sample size calculation approach using gamma distribution with a known shape parameter and explain how to extract the gamma shape estimate from previously published resources. In addition, we conduct simulations to assess the accuracy of the extracted gamma shape parameter and to explore the impact on sample size calculation when survival time distribution is misspecified.</p></div><div><h3>Results</h3><p>Our simulations show that if a previously published study (sample sizes <span><math><mrow><mo>≥</mo></mrow></math></span> 60 and censoring proportions <span><math><mrow><mo>≤</mo></mrow></math></span> 20 %) reported median and inter-quartile range of survival time, we can obtain a reasonably accurate gamma shape estimate, and use it to design new studies. When true survival time is Weibull-distributed, sample size calculation could be underestimated or overestimated depending on the hazard shape.</p></div><div><h3>Conclusions</h3><p>We show how to use gamma distribution in designing a single-arm trial, thereby offering more options beyond the exponential and Weibull. We provide a simulation-based assessment to ensure an accurate estimation of the gamma shape and recommend caution to avoid misspecification of the underlying distribution.</p></div>\",\"PeriodicalId\":37937,\"journal\":{\"name\":\"Contemporary Clinical Trials Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2451865424000917/pdfft?md5=798a1b4fa602adf63ab3debcbf80ce33&pid=1-s2.0-S2451865424000917-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Contemporary Clinical Trials Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2451865424000917\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Contemporary Clinical Trials Communications","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2451865424000917","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Sample size considerations for single-arm clinical trials with time-to-event endpoint using the gamma distribution
Background
Time-to-event (TTE) endpoints are evaluated as the primary endpoint in single-arm clinical trials; however, limited options are available in statistical software for sample size calculation. In single-arm trials with TTE endpoints, the non-parametric log-rank test is commonly used. Parametric options for single-arm design assume survival times follow exponential distribution or Weibull distribution.
Methods
The exponential- or Weibull-distributed survival time assumption does not always reflect hazard pattern of real-life diseases. We therefore propose gamma distribution as an alternative parametric option for designing single-arm studies with TTE endpoints. We outline a sample size calculation approach using gamma distribution with a known shape parameter and explain how to extract the gamma shape estimate from previously published resources. In addition, we conduct simulations to assess the accuracy of the extracted gamma shape parameter and to explore the impact on sample size calculation when survival time distribution is misspecified.
Results
Our simulations show that if a previously published study (sample sizes 60 and censoring proportions 20 %) reported median and inter-quartile range of survival time, we can obtain a reasonably accurate gamma shape estimate, and use it to design new studies. When true survival time is Weibull-distributed, sample size calculation could be underestimated or overestimated depending on the hazard shape.
Conclusions
We show how to use gamma distribution in designing a single-arm trial, thereby offering more options beyond the exponential and Weibull. We provide a simulation-based assessment to ensure an accurate estimation of the gamma shape and recommend caution to avoid misspecification of the underlying distribution.
期刊介绍:
Contemporary Clinical Trials Communications is an international peer reviewed open access journal that publishes articles pertaining to all aspects of clinical trials, including, but not limited to, design, conduct, analysis, regulation and ethics. Manuscripts submitted should appeal to a readership drawn from a wide range of disciplines including medicine, life science, pharmaceutical science, biostatistics, epidemiology, computer science, management science, behavioral science, and bioethics. Contemporary Clinical Trials Communications is unique in that it is outside the confines of disease specifications, and it strives to increase the transparency of medical research and reduce publication bias by publishing scientifically valid original research findings irrespective of their perceived importance, significance or impact. Both randomized and non-randomized trials are within the scope of the Journal. Some common topics include trial design rationale and methods, operational methodologies and challenges, and positive and negative trial results. In addition to original research, the Journal also welcomes other types of communications including, but are not limited to, methodology reviews, perspectives and discussions. Through timely dissemination of advances in clinical trials, the goal of Contemporary Clinical Trials Communications is to serve as a platform to enhance the communication and collaboration within the global clinical trials community that ultimately advances this field of research for the benefit of patients.