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Analysis of composite time-to-event endpoints in cardiovascular outcome trials. 分析心血管结果试验中的复合时间到事件终点。
IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Pub Date : 2024-10-01 Epub Date: 2024-08-08 DOI: 10.1177/17407745241267999
Rachel Marceau West, Gregory Golm, Devan V Mehrotra

Composite time-to-event endpoints are commonly used in cardiovascular outcome trials. For example, the IMPROVE-IT trial comparing ezetimibe+simvastatin to placebo+simvastatin in 18,144 patients with acute coronary syndrome used a primary composite endpoint with five component outcomes: (1) cardiovascular death, (2) non-fatal stroke, (3) non-fatal myocardial infarction, (4) coronary revascularization ≥30 days after randomization, and (5) unstable angina requiring hospitalization. In such settings, the traditional analysis compares treatments using the observed time to the occurrence of the first (i.e. earliest) component outcome for each patient. This approach ignores information for subsequent outcome(s), possibly leading to reduced power to demonstrate the benefit of the test versus the control treatment. We use real data examples and simulations to contrast the traditional approach with several alternative approaches that use data for all the intra-patient component outcomes, not just the first.

复合时间事件终点常用于心血管结果试验。例如,IMPROVE-IT 试验比较了依折麦布+ 辛伐他汀和安慰剂+ 辛伐他汀对 18,144 名急性冠脉综合征患者的治疗效果,该试验使用的主要复合终点包括五个部分:(1) 心血管死亡;(2) 非致死性卒中;(3) 非致死性心肌梗死;(4) 随机分组后≥30 天的冠状动脉血运重建;(5) 需要住院治疗的不稳定型心绞痛。在这种情况下,传统的分析方法是根据观察到的每位患者第一个(即最早的)部分结果发生的时间来比较治疗方法。这种方法忽略了后续结果的信息,可能会降低证明试验与对照治疗获益的能力。我们使用真实数据示例和模拟,将传统方法与几种替代方法进行对比,这些替代方法使用的是患者体内所有部分结果的数据,而不仅仅是第一个结果的数据。
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引用次数: 0
Is inadequate risk stratification diluting hazard ratio estimates in randomized clinical trials? 风险分层不足是否会稀释随机临床试验中的危险比估计值?
IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Pub Date : 2024-10-01 Epub Date: 2024-02-02 DOI: 10.1177/17407745231222448
Devan V Mehrotra, Rachel Marceau West

In randomized clinical trials, analyses of time-to-event data without risk stratification, or with stratification based on pre-selected factors revealed at the end of the trial to be at most weakly associated with risk, are quite common. We caution that such analyses are likely delivering hazard ratio estimates that unwittingly dilute the evidence of benefit for the test relative to the control treatment. To make our case, first, we use a hypothetical scenario to contrast risk-unstratified and risk-stratified hazard ratios. Thereafter, we draw attention to the previously published 5-step stratified testing and amalgamation routine (5-STAR) approach in which a pre-specified treatment-blinded algorithm is applied to survival times from the trial to partition patients into well-separated risk strata using baseline covariates determined to be jointly strongly prognostic for event risk. After treatment unblinding, a treatment comparison is done within each risk stratum and stratum-level results are averaged for overall inference. For illustration, we use 5-STAR to reanalyze data for the primary and key secondary time-to-event endpoints from three published cardiovascular outcomes trials. The results show that the 5-STAR estimate is typically smaller (i.e. more in favor of the test treatment) than the originally reported (traditional) estimate. This is not surprising because 5-STAR mitigates the presumed dilution bias in the traditional hazard ratio estimate caused by no or inadequate risk stratification, as evidenced by two detailed examples. Pre-selection of stratification factors at the trial design stage to achieve adequate risk stratification for the analysis will often be challenging. In such settings, an objective risk stratification approach such as 5-STAR, which is partly aligned with guidance from the US Food and Drug Administration on covariate-adjustment in clinical trials, is worthy of consideration.

在随机临床试验中,对时间到事件数据进行分析而不进行风险分层,或根据试验结束时发现与风险最多只存在微弱关联的预选因素进行分层的做法非常普遍。我们要提醒的是,这种分析很可能会提供危险比估计值,无意中稀释了试验相对于对照治疗的获益证据。为了说明我们的观点,首先,我们用一个假设情景来对比风险未分层和风险分层的危险比。随后,我们提请大家注意之前发表的五步分层检验和合并常规(5-STAR)方法,该方法将预先指定的治疗盲法应用于试验的生存时间,利用被确定为对事件风险有共同强预后作用的基线协变量将患者划分为风险分层。治疗解除绑定后,在每个风险分层内进行治疗比较,并对分层结果取平均值进行总体推断。为了说明问题,我们使用 5-STAR 重新分析了三项已发表的心血管结局试验的主要和关键次要时间-事件终点数据。结果显示,5-STAR 估计值通常比最初报告的(传统)估计值要小(即更有利于 5-STAR 试验治疗)。这并不奇怪,因为 5-STAR 可减轻传统危险比估计值中因未进行风险分层或风险分层不充分而导致的假定稀释偏差,两个详细的例子就证明了这一点。在试验设计阶段预先选择分层因素,为分析实现充分的风险分层往往具有挑战性。在这种情况下,5-STAR 等客观风险分层方法值得考虑,该方法部分符合美国食品药品管理局关于临床试验中协变量调整的指导意见。
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引用次数: 0
Using multistate models with clinical trial data for a deeper understanding of complex disease processes. 将多态模型与临床试验数据相结合,加深对复杂疾病过程的理解。
IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Pub Date : 2024-10-01 Epub Date: 2024-08-02 DOI: 10.1177/17407745241267862
Terry M Therneau, Fang-Shu Ou

A clinical trial represents a large commitment from all individuals involved and a huge financial obligation given its high cost; therefore, it is wise to make the most of all collected data by learning as much as possible. A multistate model is a generalized framework to describe longitudinal events; multistate hazards models can treat multiple intermediate/final clinical endpoints as outcomes and estimate the impact of covariates simultaneously. Proportional hazards models are fitted (one per transition), which can be used to calculate the absolute risks, that is, the probability of being in a state at a given time, the expected number of visits to a state, and the expected amount of time spent in a state. Three publicly available clinical trial datasets, colon, myeloid, and rhDNase, in the survival package in R were used to showcase the utility of multistate hazards models. In the colon dataset, a very well-known and well-used dataset, we found that the levamisole+fluorouracil treatment extended time in the recurrence-free state more than it extended overall survival, which resulted in less time in the recurrence state, an example of the classic "compression of morbidity." In the myeloid dataset, we found that complete response (CR) is durable, patients who received treatment B have longer sojourn time in CR than patients who received treatment A, while the mutation status does not impact the transition rate to CR but is highly influential on the sojourn time in CR. We also found that more patients in treatment A received transplants without CR, and more patients in treatment B received transplants after CR. In addition, the mutation status is highly influential on the CR to transplant transition rate. The observations that we made on these three datasets would not be possible without multistate models. We want to encourage readers to spend more time to look deeper into clinical trial data. It has a lot more to offer than a simple yes/no answer if only we, the statisticians, are willing to look for it.

临床试验是所有参与人员的一项重大承诺,也是一项巨大的财务义务,因为其成本高昂;因此,通过尽可能多的学习来充分利用所有收集到的数据是明智之举。多态模型是描述纵向事件的通用框架;多态危险模型可将多个中间/最终临床终点作为结果,并同时估计协变量的影响。比例危险模型是拟合模型(每个转变一个),可用于计算绝对风险,即在给定时间内处于某一状态的概率、进入某一状态的预期次数以及在某一状态下花费的预期时间。为了展示多态危险模型的实用性,我们使用了 R 生存软件包中三个公开的临床试验数据集:结肠、骨髓和 rhDNase。结肠数据集是一个非常著名且使用广泛的数据集,在该数据集中,我们发现左旋咪唑+氟尿嘧啶治疗延长了无复发状态的时间,超过了延长总生存期的时间,从而减少了复发状态的时间,这就是典型的 "压缩发病率 "的例子。在骨髓数据集中,我们发现完全应答(CR)是持久的,接受 B 治疗的患者比接受 A 治疗的患者在 CR 状态下的停留时间更长,而突变状态并不影响向 CR 的转变率,但对 CR 状态下的停留时间有很大影响。我们还发现,接受治疗 A 的更多患者在没有 CR 的情况下接受了移植,而接受治疗 B 的更多患者在 CR 后接受了移植。此外,突变状态对 CR 到移植的转换率也有很大影响。如果没有多态模型,我们就不可能对这三个数据集进行观察。我们鼓励读者花更多时间深入研究临床试验数据。只要我们统计学家愿意去寻找,它就能提供比简单的 "是/否 "答案更多的信息。
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引用次数: 0
Statistical approaches for component-wise censored composite endpoints. 成分删减复合终点的统计方法。
IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Pub Date : 2024-10-01 Epub Date: 2024-08-08 DOI: 10.1177/17407745241265628
Anne Eaton

Composite endpoints defined as the time to the earliest of two or more events are often used as primary endpoints in clinical trials. Component-wise censoring arises when different components of the composite endpoint are censored differently. We focus on a composite of death and a non-fatal event where death time is right censored and the non-fatal event time is interval censored because the event can only be detected during study visits. Such data are most often analysed using methods for right censored data, treating the time the non-fatal event was first detected as the time it occurred. This can lead to bias, particularly when the time between assessments is long. We describe several approaches for estimating the event-free survival curve and the effect of treatment on event-free survival via the hazard ratio that are specifically designed to handle component-wise censoring. We apply the methods to a randomized study of breastfeeding versus formula feeding for infants of mothers infected with human immunodeficiency virus.

复合终点定义为两个或两个以上事件最早发生的时间,在临床试验中常被用作主要终点。当综合终点的不同组成部分采用不同的剔除方式时,就会出现成分剔除。我们重点研究死亡和非致命事件的复合终点,其中死亡时间采用右侧剔除,而非致命事件时间采用区间剔除,因为只有在研究访问期间才能检测到该事件。此类数据通常使用右删减数据的方法进行分析,将首次检测到非致命事件的时间视为事件发生的时间。这可能会导致偏差,尤其是当评估间隔时间较长时。我们介绍了几种估算无事件生存曲线的方法,以及通过危险比估算治疗对无事件生存的影响的方法,这些方法是专门为处理成分删减而设计的。我们将这些方法应用到一项随机研究中,研究对象是感染人类免疫缺陷病毒的母亲所生的婴儿,研究方法是母乳喂养还是配方奶粉喂养。
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引用次数: 0
Estimands in clinical trials of complex disease processes. 复杂疾病过程临床试验中的估算。
IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Pub Date : 2024-10-01 Epub Date: 2024-08-24 DOI: 10.1177/17407745241268054
Richard J Cook, Jerald F Lawless

Clinical trials with random assignment of treatment provide evidence about causal effects of an experimental treatment compared to standard care. However, when disease processes involve multiple types of possibly semi-competing events, specification of target estimands and causal inferences can be challenging. Intercurrent events such as study withdrawal, the introduction of rescue medication, and death further complicate matters. There has been much discussion about these issues in recent years, but guidance remains ambiguous. Some recommended approaches are formulated in terms of hypothetical settings that have little bearing in the real world. We discuss issues in formulating estimands, beginning with intercurrent events in the context of a linear model and then move on to more complex disease history processes amenable to multistate modeling. We elucidate the meaning of estimands implicit in some recommended approaches for dealing with intercurrent events and highlight the disconnect between estimands formulated in terms of potential outcomes and the real world.

采用随机分配治疗方法的临床试验可提供实验性治疗与标准治疗相比的因果效应证据。然而,当疾病过程涉及多种类型的可能半竞争事件时,目标估计值的指定和因果关系的推断就会面临挑战。同时发生的事件,如研究退出、使用抢救药物和死亡等,会使问题进一步复杂化。近年来,关于这些问题的讨论很多,但指导意见仍然模糊不清。一些推荐方法是在假设环境下制定的,与现实世界关系不大。我们将从线性模型背景下的并发症开始,讨论制定估计值的问题,然后再讨论适合多态模型的更复杂的疾病史过程。我们阐明了一些处理并发症的推荐方法中隐含的估计指标的含义,并强调了根据潜在结果制定的估计指标与现实世界之间的脱节。
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引用次数: 0
Inferences for the distribution of the duration of response in a comparative clinical study. 比较临床研究中反应持续时间分布的推论。
IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Pub Date : 2024-10-01 Epub Date: 2024-08-08 DOI: 10.1177/17407745241264188
Ying Cui, Bo Huang, Lu Mao, Hajime Uno, Lee-Jen Wei, Lu Tian

Duration of response is an important endpoint used in drug development. Prolonged duration for response is often viewed as an early indication of treatment efficacy. However, there are numerous difficulties in studying the distribution of duration of response based on observed data subject to right censoring in practice. The most important obstacle is that the distribution of the duration of response is in general not identifiable in the presence of censoring due to the simple fact that there is no information on the joint distribution of time to response and time to progression beyond the largest follow-up time. In this article, we introduce the restricted duration of response as a replacement of the conventional duration of response. The distribution of restricted duration of response is estimable and we have proposed several nonparametric estimators in this article. The corresponding inference procedure and additional downstream analysis have been developed. Extensive numerical simulations have been conducted to examine the finite sample performance of the proposed estimators. It appears that a new regression-based two-step estimator for the survival function of the restricted duration of response tends to have a robust and superior performance, and we recommend its use in practice. A real data example from oncology has been used to illustrate the analysis for restricted duration of response.

反应持续时间是药物研发中的一个重要终点。反应持续时间的延长通常被视为疗效的早期指标。然而,在实践中,根据右删减的观察数据研究反应持续时间的分布存在许多困难。最重要的障碍是,在存在剔除的情况下,一般无法确定反应持续时间的分布,原因很简单,因为没有关于最大随访时间之外的反应时间和进展时间联合分布的信息。在本文中,我们引入了限制性反应持续时间来替代传统的反应持续时间。限制性反应持续时间的分布是可以估计的,我们在本文中提出了几种非参数估计器。我们还开发了相应的推断程序和额外的下游分析。我们进行了大量的数值模拟,以检验所提出的估计器的有限样本性能。结果表明,新的基于回归的两步估计法对受限反应持续时间的生存函数具有稳健而优越的性能,我们建议在实践中使用该估计法。我们使用了一个肿瘤学的真实数据示例来说明受限反应持续时间的分析。
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引用次数: 0
Multiply robust estimation of principal causal effects with noncompliance and survival outcomes. 利用不合规和生存结果对主要因果效应进行稳健的多重估计。
IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Pub Date : 2024-10-01 Epub Date: 2024-05-30 DOI: 10.1177/17407745241251773
Chao Cheng, Yueqi Guo, Bo Liu, Lisa Wruck, Fan Li, Fan Li

Treatment noncompliance and censoring are two common complications in clinical trials. Motivated by the ADAPTABLE pragmatic clinical trial, we develop methods for assessing treatment effects in the presence of treatment noncompliance with a right-censored survival outcome. We classify the participants into principal strata, defined by their joint potential compliance status under treatment and control. We propose a multiply robust estimator for the causal effects on the survival probability scale within each principal stratum. This estimator is consistent even if one, sometimes two, of the four working models-on the treatment assignment, the principal strata, censoring, and the outcome-is misspecified. A sensitivity analysis strategy is developed to address violations of key identification assumptions, the principal ignorability and monotonicity. We apply the proposed approach to the ADAPTABLE trial to study the causal effect of taking low- versus high-dosage aspirin on all-cause mortality and hospitalization from cardiovascular diseases.

治疗不达标和删减是临床试验中常见的两种并发症。受 ADAPTABLE 实用临床试验的启发,我们开发了在治疗不依从的情况下评估治疗效果的方法,并对生存结果进行了右删减。我们将参与者划分为主要阶层,根据他们在治疗和对照下的联合潜在依从性状态进行定义。我们对每个主要分层内生存概率标度上的因果效应提出了一个多重稳健估计器。即使治疗分配、主要分层、普查和结果这四个工作模型中的一个(有时是两个)模型被错误地指定,这个估计值也是一致的。我们制定了一种敏感性分析策略,以解决违反关键识别假设(主要无知性和单调性)的问题。我们将提出的方法应用于 ADAPTABLE 试验,研究服用低剂量阿司匹林与服用大剂量阿司匹林对全因死亡率和心血管疾病住院率的因果效应。
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引用次数: 0
Defining estimand for the win ratio: Separate the true effect from censoring. 定义胜率估计值:将真实效应与普查区分开来。
IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Pub Date : 2024-10-01 Epub Date: 2024-07-30 DOI: 10.1177/17407745241259356
Lu Mao

The win ratio has been increasingly used in trials with hierarchical composite endpoints. While the outcomes involved and the rule for their comparisons vary with the application, there is invariably little attention to the estimand of the resulting statistic, causing difficulties in interpretation and cross-trial comparison. We make the case for articulating the estimand as a first step to win ratio analysis and establish that the root cause for its elusiveness is its intrinsic dependency on the time frame of comparison, which, if left unspecified, is set haphazardly by trial-specific censoring. From the statistical literature, we summarize two general approaches to overcome this uncertainty-a nonparametric one that pre-specifies the time frame for all comparisons, and a semiparametric one that posits a constant win ratio across all times-each with publicly available software and real examples. Finally, we discuss unsolved challenges, such as estimand construction and inference in the presence of intercurrent events.

在采用分层综合终点的试验中,胜率的使用越来越多。虽然所涉及的结果及其比较规则因应用而异,但人们总是很少关注统计结果的估计值,这给解释和跨试验比较造成了困难。我们提出的理由是,阐明估计值是胜诉率分析的第一步,并确定其难以捉摸的根本原因是其内在依赖于比较的时间框架,如果不指定时间框架,就会通过特定审判的普查而随意设定。从统计文献中,我们总结了克服这种不确定性的两种一般方法--一种是预先指定所有比较时限的非参数方法,另一种是假设所有时间内胜率不变的半参数方法--每种方法都有公开可用的软件和实际案例。最后,我们讨论了尚未解决的难题,如估计值构建和存在并发事件时的推断。
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引用次数: 0
15th Annual University of Pennsylvania conference on statistical issues in clinical trial/advances in time to event analyses in clinical trials (morning panel discussion). 宾夕法尼亚大学第 15 届年会,主题为临床试验中的统计问题/临床试验中事件时间分析的进展(上午小组讨论)。
IF 2.2 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Pub Date : 2024-10-01 Epub Date: 2024-08-30 DOI: 10.1177/17407745241272012
Pralay Mukhopadhyay, Douglas Schaubel, Mei-Cheng Wang
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引用次数: 0
Proceedings of the University of Pennsylvania 15th annual conference on statistical issues in clinical trials: Advances in time-to-event analyses in clinical trials-challenges and opportunities. 宾夕法尼亚大学第 15 届临床试验统计问题年会论文集:临床试验中从时间到事件分析的进展--挑战与机遇。
IF 2.7 3区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Pub Date : 2024-09-18 DOI: 10.1177/17407745241276119
Mary E Putt
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引用次数: 0
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Clinical Trials
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