{"title":"CUSUMIN Combination: A Cumulative Sum Interval Design for Phase I Cancer Drug-Combination Trials.","authors":"Tomoyoshi Hatayama, Seiichi Yasui","doi":"10.1002/pst.70007","DOIUrl":null,"url":null,"abstract":"<p><p>Recently, model-assisted designs, including a Bayesian optimal interval (BOIN) design with optimal thresholds for determining the dose for the next cohort, have been proposed for Phase I cancer studies. Model-assisted designs are useful owing to their good performance in addition to their algorithm-based simplicity. In this era of precision medicine, drug combinations are widely used to enhance treatment efficacy and overcome resistance to monotherapies. However, identification of maximum tolerated dose (MTD) combinations is complicated because the joint toxicity order of paired doses is only partially known. BOIN and Keyboard combination designs are the only model-assisted designs developed to date. Further, both these combination designs show similar operational characteristics. Despite the simplicity and superior performance of model-assisted designs, they have not been sufficiently studied in Phase I drug combination trials. In this study, to provide a new design with simplicity and superior performance compared to model-assisted designs for dose-combination cancer Phase I studies, we extend the cumulative sum interval design (CUSUMIN) developed for single-agent dose-finding design based on statistical quality control methodology, which improves on BOIN and other representative model-assisted designs in terms of controlling overdosing rates while maintaining similar performance in determining the MTD. CUSUMIN can be expected to provide a safer assignment than that of BOIN in drug combination dose-finding studies while maintaining MTD selection performance, as shown in the single-agent dose-finding settings.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 3","pages":"e70007"},"PeriodicalIF":1.3000,"publicationDate":"2025-05-01","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.70007","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
Abstract
Recently, model-assisted designs, including a Bayesian optimal interval (BOIN) design with optimal thresholds for determining the dose for the next cohort, have been proposed for Phase I cancer studies. Model-assisted designs are useful owing to their good performance in addition to their algorithm-based simplicity. In this era of precision medicine, drug combinations are widely used to enhance treatment efficacy and overcome resistance to monotherapies. However, identification of maximum tolerated dose (MTD) combinations is complicated because the joint toxicity order of paired doses is only partially known. BOIN and Keyboard combination designs are the only model-assisted designs developed to date. Further, both these combination designs show similar operational characteristics. Despite the simplicity and superior performance of model-assisted designs, they have not been sufficiently studied in Phase I drug combination trials. In this study, to provide a new design with simplicity and superior performance compared to model-assisted designs for dose-combination cancer Phase I studies, we extend the cumulative sum interval design (CUSUMIN) developed for single-agent dose-finding design based on statistical quality control methodology, which improves on BOIN and other representative model-assisted designs in terms of controlling overdosing rates while maintaining similar performance in determining the MTD. CUSUMIN can be expected to provide a safer assignment than that of BOIN in drug combination dose-finding studies while maintaining MTD selection performance, as shown in the single-agent dose-finding settings.
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.