肿瘤学中限制平均反应时间(RMDoR)的估计。

IF 1.4 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2025-01-01 DOI:10.1002/pst.2468
Antonios Daletzakis, Kit C B Roes, Marianne A Jonker
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引用次数: 0

摘要

反应持续时间(DoR)定义为从开始对治疗产生反应到疾病进展或因任何原因死亡(以较早者为准)的时间。预期DoR可能是衡量治疗效果的合适估计,但在实践中很难估计,因为患者的随访时间通常是正确审查的。相反,通常使用受限平均反应持续时间(RMDoR)。在时间τ $$ \tau $$中的RMDoR等于限制于区间0 τ $$ \left[0,\tau \right] $$的期望DoR。在本文中,我们将RMDoR的行为视为τ $$ \tau $$的函数及其作为量化治疗效果的度量的适用性。此外,我们还重点研究了RMDoR的估计。在肿瘤学中,对治疗的反应和疾病的进展通常是通过定时扫描来检测的,因此是间隔审查的。我们描述了RMDoR的多个估计器,这些估计器以不同的方式处理区间审查,并研究了这些估计器在单臂试验和随机对照试验中的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Estimation of the Restricted Mean Duration of Response (RMDoR) in Oncology.

The duration of response (DoR) is defined as the time from the onset of response to treatment up to progression of disease or death due to any reason, whichever occurs earlier. The expected DoR could be a suitable estimand to measure the efficacy of a treatment but is in practice difficult to estimate, since patients' follow-up times are often right-censored. Instead, the restricted mean duration of response (RMDoR) is often used. The RMDoR in a time τ $$ \tau $$ is equal to the expected DoR restricted to the interval 0 τ $$ \left[0,\tau \right] $$ . In this paper, we consider the behaviour of the RMDoR as a function of τ $$ \tau $$ and its suitability as a measure to quantify the efficacy of a treatment. Besides, we focus on the estimation of the RMDoR. In oncology, the events response to treatment and progression of disease are typically detected through time-scheduled scans and are therefore interval-censored. We describe multiple estimators for the RMDoR that deal with the interval censoring in different ways and study the performance of these estimators in single arm trials and randomised controlled trials.

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来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
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.
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