扩大癌症 I 期试验的剂量个体化资格。

IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Statistics in Medicine Pub Date : 2024-10-31 DOI:10.1002/sim.10264
Rebecca B Silva, Bin Cheng, Richard D Carvajal, Shing M Lee
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引用次数: 0

摘要

人们主张扩大癌症试验的资格标准,以便更准确地代表预期的患者群体。在普及性和招募方面的优势显而易见,但在设计效率和患者安全方面也有一些重要的考虑因素。虽然预计这些亚人群的毒性可能是相同的,但如果存在毒性特征不同的亚人群,设计应能够推荐安全和精确的剂量。已经有人提出了考虑患者异质性的剂量寻找设计,但现有方法假定异质性的来源是已知的。我们提出了一种扩大资格的剂量寻找设计,以解决 I 期癌症临床试验中患者异质性未知的情况,在这种情况下,资格范围扩大了,多种资格标准有可能导致患者亚群的最佳剂量不同。该设计提供了一种二合一的剂量寻找方法,即同时选择可区分最大耐受剂量(MTD)的患者标准,使用随机搜索变量选择,并在需要时推荐特定亚群的 MTD。我们的模拟研究将拟议的设计与假定患者同质性的天真方法进行了比较,结果表明,在各种情况下,拟议的设计都具有良好的运行特性,能在试验期间更频繁地将患者分配到其真正的 MTD,在需要时推荐一种以上的 MTD,并能确定区分患者群体的标准。建议的设计突出了在早期阶段增加更多可变性的优势,并展示了假设患者同质性会如何导致不安全或亚治疗剂量推荐。
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Dose Individualization for Phase I Cancer Trials With Broadened Eligibility.

Broadening eligibility criteria in cancer trials has been advocated to represent the intended patient population more accurately. The advantages are clear in terms of generalizability and recruitment, however there are some important considerations in terms of design for efficiency and patient safety. While toxicity may be expected to be homogeneous across these subpopulations, designs should be able to recommend safe and precise doses if subpopulations with different toxicity profiles exist. Dose-finding designs accounting for patient heterogeneity have been proposed, but existing methods assume that the source of heterogeneity is known. We propose a broadened eligibility dose-finding design to address the situation of unknown patient heterogeneity in phase I cancer clinical trials where eligibility is expanded, and multiple eligibility criteria could potentially lead to different optimal doses for patient subgroups. The design offers a two-in-one approach to dose-finding by simultaneously selecting patient criteria that differentiate the maximum tolerated dose (MTD), using stochastic search variable selection, and recommending the subpopulation-specific MTD if needed. Our simulation study compares the proposed design to the naive approach of assuming patient homogeneity and demonstrates favorable operating characteristics across a wide range of scenarios, allocating patients more often to their true MTD during the trial, recommending more than one MTD when needed, and identifying criteria that differentiate the patient population. The proposed design highlights the advantages of adding more variability at an early stage and demonstrates how assuming patient homogeneity can lead to unsafe or sub-therapeutic dose recommendations.

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来源期刊
Statistics in Medicine
Statistics in Medicine 医学-公共卫生、环境卫生与职业卫生
CiteScore
3.40
自引率
10.00%
发文量
334
审稿时长
2-4 weeks
期刊介绍: The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.
期刊最新文献
A Novel Bayesian Spatio-Temporal Surveillance Metric to Predict Emerging Infectious Disease Areas of High Disease Risk. Does Remdesivir Lower COVID-19 Mortality? A Subgroup Analysis of Hospitalized Adults Receiving Supplemental Oxygen. Modeling Chronic Disease Mortality by Methods From Accelerated Life Testing. A Nonparametric Global Win Probability Approach to the Analysis and Sizing of Randomized Controlled Trials With Multiple Endpoints of Different Scales and Missing Data: Beyond O'Brien-Wei-Lachin. Causal Inference for Continuous Multiple Time Point Interventions.
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