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Stakeholders' Perspectives on Current Issues in Data Monitoring Committees 利益相关者对数据监测委员会当前问题的看法。
IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-22 DOI: 10.1002/bimj.202300384
Michael J. Cartwright, Tim Friede, David Lawrence, Emma May, Tobias Mütze, Kit Roes

Data Monitoring Committees (DMCs) are groups of experts that review accumulating data from one or more ongoing clinical studies and advise the Sponsor regarding the continuing safety of study subjects along with the continuing validity and scientific merit of the study. Although DMCs are widely used, considerable variability exists in their conduct. This paper offers recommendations, derived from sessions given at the 2023 Central European Network International Biometric and Statisticians in the Pharmaceutical Industry Conferences' and the authors' experiences. We focus on four topics that are part of the DMC process and where there is unclarity and inconsistency in current practices: (1) Communication with the DMC—We reflect on the importance of effective, proper communication channels between the DMC and relevant stakeholders to foster collaboration and exchange of critical information while retaining study integrity throughout. (2) Open sessions—We discuss the benefits of incorporating open sessions in DMC meetings to enhance transparency, inclusivity, and the consideration of diverse perspectives, as well as pitfalls of open sessions. (3) Access to efficacy data—We highlight the need for appropriate access to efficacy data by DMCs and discuss how to implement this in practice and how to address potential concerns regarding multiplicity. (4) Interactive data displays—We outline the utilization of interactive data displays to facilitate a more intuitive understanding of study results by the DMC. By addressing these topics, we aim to provide comprehensive practical recommendations that bridge the gap between current practices and optimal DMC functionality.

数据监测委员会(DMC)是由专家组成的小组,负责审查一项或多项正在进行的临床研究的累积数据,并就研究对象的持续安全性以及研究的持续有效性和科学价值向申办方提供建议。尽管 DMC 被广泛使用,但其行为方式存在相当大的差异。本文根据 2023 年中欧网络国际制药业生物计量和统计学家会议上的发言和作者的经验提出建议。我们重点讨论了 DMC 流程中的四个主题,以及当前实践中存在的不清晰和不一致之处:(1)与 DMC 的沟通--我们思考了 DMC 与相关利益方之间有效、适当的沟通渠道的重要性,以促进合作和重要信息的交流,同时在整个过程中保持研究的完整性。(2) 公开会议--我们讨论了将公开会议纳入 DMC 会议以提高透明度、包容性和考虑不同观点的好处,以及公开会议的缺陷。(3) 获取疗效数据--我们强调了区管会适当获取疗效数据的必要性,并讨论了如何在实践中落实这一点,以及如何解决潜在的多重性问题。(4) 交互式数据显示--我们概述了如何利用交互式数据显示来帮助 DMC 更直观地了解研究结果。通过讨论这些主题,我们旨在提供全面实用的建议,缩小当前实践与最佳 DMC 功能之间的差距。
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引用次数: 0
A Network-Constrain Weibull AFT Model for Biomarkers Discovery 用于生物标记物发现的网络应变 Weibull AFT 模型
IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-22 DOI: 10.1002/bimj.202300272
Claudia Angelini, Daniela De Canditiis, Italia De Feis, Antonella Iuliano

We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation. When using the log-linear representation, the inference problem becomes a structured sparse regression problem for which we explicitly incorporate the correlation patterns among predictors using a double penalty that promotes both sparsity and grouping effect. Moreover, we establish the theoretical consistency for the AFTNet estimator and present an efficient iterative computational algorithm based on the proximal gradient descent method. Finally, we evaluate AFTNet performance both on synthetic and real data examples.

我们提出的 AFTNet 是一种新颖的网络约束生存分析方法,它基于 Weibull 加速失效时间(AFT)模型,通过惩罚似然法解决变量选择和估计问题。当使用对数线性表示时,推理问题就变成了一个结构稀疏回归问题,我们使用一种既能促进稀疏性又能促进分组效应的双重惩罚,明确地纳入了预测因子之间的相关模式。此外,我们还建立了 AFTNet 估计器的理论一致性,并提出了一种基于近似梯度下降法的高效迭代计算算法。最后,我们对 AFTNet 在合成数据和真实数据示例上的性能进行了评估。
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引用次数: 0
Multivariate Scalar on Multidimensional Distribution Regression With Application to Modeling the Association Between Physical Activity and Cognitive Functions 多维分布的多变量标量回归应用于体育锻炼与认知功能之间关系的建模。
IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-22 DOI: 10.1002/bimj.202400042
Rahul Ghosal, Marcos Matabuena

We develop a new method for multivariate scalar on multidimensional distribution regression. Traditional approaches typically analyze isolated univariate scalar outcomes or consider unidimensional distributional representations as predictors. However, these approaches are suboptimal because (i) they fail to utilize the dependence between the distributional predictors and (ii) neglect the correlation structure of the response. To overcome these limitations, we propose a multivariate distributional analysis framework that harnesses the power of multivariate density functions and multitask learning. We develop a computationally efficient semiparametric estimation method for modeling the effect of the latent joint density on the multivariate response of interest. Additionally, we introduce a new conformal prediction algorithm for quantifying the uncertainty of our multivariate predictions based on subject characteristics and individualized distributional predictors, providing valuable insights into the conditional distribution of the response. We validate the effectiveness of our proposed method through comprehensive numerical simulations, clearly demonstrating its superior performance compared to traditional methods. The application of the proposed method is demonstrated on triaxial accelerometer data from the National Health and Nutrition Examination Survey 2011–2014 for modeling the association between cognitive scores across various domains and distributional representation of physical activity among the older adult population. Our results highlight the advantages of the proposed approach, emphasizing the significance of incorporating multidimensional distributional information in the triaxial accelerometer data.

我们为多维标量的多维分布回归开发了一种新方法。传统方法通常分析孤立的单变量标量结果,或将单维分布表示视为预测因子。然而,这些方法并不理想,因为 (i) 它们未能利用分布预测因子之间的依赖关系,(ii) 忽视了响应的相关结构。为了克服这些局限性,我们提出了一个多变量分布分析框架,利用多变量密度函数和多任务学习的力量。我们开发了一种计算高效的半参数估计方法,用于模拟潜在联合密度对相关多元响应的影响。此外,我们还引入了一种新的共形预测算法,用于量化基于受试者特征和个性化分布预测因子的多元预测的不确定性,从而为了解反应的条件分布提供有价值的见解。我们通过全面的数值模拟验证了我们提出的方法的有效性,清楚地证明了它与传统方法相比的优越性能。我们在 2011-2014 年全国健康与营养调查的三轴加速度计数据上演示了所提方法的应用,以模拟老年人群中不同领域的认知分数与体力活动分布表示之间的关联。我们的结果凸显了所提方法的优势,强调了在三轴加速度计数据中纳入多维分布信息的重要性。
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引用次数: 0
Issue Information: Biometrical Journal 7'24 发行信息:生物计量学杂志 7'24
IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-17 DOI: 10.1002/bimj.202470007
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引用次数: 0
Investigating the Heterogeneity of “Study Twins” 调查 "研究双胞胎 "的异质性。
IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-09-02 DOI: 10.1002/bimj.202300387
Christian Röver, Tim Friede

Meta-analyses are commonly performed based on random-effects models, while in certain cases one might also argue in favor of a common-effect model. One such case may be given by the example of two “study twins” that are performed according to a common (or at least very similar) protocol. Here we investigate the particular case of meta-analysis of a pair of studies, for example, summarizing the results of two confirmatory clinical trials in phase III of a clinical development program. Thereby, we focus on the question of to what extent homogeneity or heterogeneity may be discernible and include an empirical investigation of published (“twin”) pairs of studies. A pair of estimates from two studies only provide very little evidence of homogeneity or heterogeneity of effects, and ad hoc decision criteria may often be misleading.

元分析通常是基于随机效应模型进行的,但在某些情况下,我们也可以主张采用共效模型。两个 "双胞胎研究 "的例子就是这样一个例子,这两个 "双胞胎研究 "是按照共同(或至少非常相似)的方案进行的。在此,我们将研究对一对研究进行荟萃分析的特殊情况,例如,总结临床开发计划第三阶段两项确证性临床试验的结果。因此,我们将重点放在同质性或异质性在多大程度上可以辨别的问题上,并对已发表的("孪生")成对研究进行实证调查。来自两项研究的一对估计值只能提供很少的证据来证明效应的同质性或异质性,而特别的判定标准往往会产生误导。
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引用次数: 0
False Discovery Rate Control for Lesion-Symptom Mapping With Heterogeneous Data via Weighted p-Values 通过加权 p 值控制异构数据病变-症状映射的错误发现率
IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-20 DOI: 10.1002/bimj.202300198
Siyu Zheng, Alexander C. McLain, Joshua Habiger, Christopher Rorden, Julius Fridriksson

Lesion-symptom mapping studies provide insight into what areas of the brain are involved in different aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is nonuniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Recent advancements in multiple testing methodologies allow researchers to weigh hypotheses according to side information (e.g., information on power heterogeneity). In this paper, we propose the use of p-value weighting for voxel-based lesion-symptom mapping studies. The weights are created using the distribution of lesion status and spatial information to estimate different non-null prior probabilities for each hypothesis test through some common approaches. We provide a monotone minimum weight criterion, which requires minimum a priori power information. Our methods are demonstrated on dependent simulated data and an aphasia study investigating which regions of the brain are associated with the severity of language impairment among stroke survivors. The results demonstrate that the proposed methods have robust error control and can increase power. Further, we showcase how weights can be used to identify regions that are inconclusive due to lack of power.

病变-症状图谱研究有助于深入了解大脑的哪些区域与认知的不同方面有关。这通常是通过对自然发生的脑损伤或病变(如中风或脑肿瘤)患者进行行为测试来实现的。这就产生了高维观察数据,其中病变状态(存在/不存在)分布不均匀,一些体素在极少数(或没有)受试者中存在病变。在这种情况下,大规模单变量假设检验具有严重的功率异质性,许多检验先验已知几乎没有功率。多重测试方法的最新进展使研究人员能够根据侧面信息(如功率异质性信息)对假设进行权衡。在本文中,我们建议在基于体素的病变症状图谱研究中使用 p 值加权法。权重是利用病变状态和空间信息的分布来创建的,通过一些常见的方法来估计每个假设检验的不同非空先验概率。我们提供了一个单调最小权重标准,它要求最小的先验功率信息。我们的方法在依赖性模拟数据和一项失语症研究中得到了验证,该研究调查了大脑的哪些区域与中风幸存者语言障碍的严重程度相关。结果表明,所提出的方法具有强大的误差控制能力,并能提高功率。此外,我们还展示了如何利用权重来识别由于缺乏力量而无法得出结论的区域。
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引用次数: 0
Random Survival Forests With Competing Events: A Subdistribution-Based Imputation Approach 具有竞争事件的随机生存森林:基于子分布的估算方法
IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-20 DOI: 10.1002/bimj.202400014
Charlotte Behning, Alexander Bigerl, Marvin N. Wright, Peggy Sekula, Moritz Berger, Matthias Schmid

Random survival forests (RSF) can be applied to many time-to-event research questions and are particularly useful in situations where the relationship between the independent variables and the event of interest is rather complex. However, in many clinical settings, the occurrence of the event of interest is affected by competing events, which means that a patient can experience an outcome other than the event of interest. Neglecting the competing event (i.e., regarding competing events as censoring) will typically result in biased estimates of the cumulative incidence function (CIF). A popular approach for competing events is Fine and Gray's subdistribution hazard model, which directly estimates the CIF by fitting a single-event model defined on a subdistribution timescale. Here, we integrate concepts from the subdistribution hazard modeling approach into the RSF. We develop several imputation strategies that use weights as in a discrete-time subdistribution hazard model to impute censoring times in cases where a competing event is observed. Our simulations show that the CIF is well estimated if the imputation already takes place outside the forest on the overall dataset. Especially in settings with a low rate of the event of interest or a high censoring rate, competing events must not be neglected, that is, treated as censoring. When applied to a real-world epidemiological dataset on chronic kidney disease, the imputation approach resulted in highly plausible predictor–response relationships and CIF estimates of renal events.

随机生存森林(RSF)可应用于许多从时间到事件的研究问题,尤其适用于自变量与相关事件之间关系相当复杂的情况。然而,在许多临床环境中,相关事件的发生会受到竞争事件的影响,这意味着患者可能会经历除相关事件之外的其他结果。忽略竞争事件(即把竞争事件视为普查)通常会导致对累积发病率函数(CIF)的估计出现偏差。针对竞争事件的一种流行方法是 Fine 和 Gray 的子分布危险模型,该模型通过拟合定义在子分布时间尺度上的单一事件模型来直接估计 CIF。在此,我们将亚分布危害建模方法的概念整合到 RSF 中。我们开发了几种估算策略,在观测到竞争事件的情况下,使用离散时间子分布危害模型中的权重来估算删减时间。我们的模拟结果表明,如果在整个数据集上的森林外已经进行了估算,那么 CIF 就能得到很好的估计。特别是在相关事件发生率较低或剔除率较高的情况下,竞争事件不应被忽视,即应被视为剔除事件。在应用于真实世界的慢性肾病流行病学数据集时,估算方法得出了高度可信的预测因子-响应关系和肾病事件的 CIF 估计值。
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引用次数: 0
Semiparametric Additive Modeling of the Restricted Mean Survival Time 受限平均生存时间的半参数加法模型
IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-16 DOI: 10.1002/bimj.202200371
Yuan Zhang, Douglas E. Schaubel

Analysis of the restricted mean survival time (RMST) has become increasingly common in biomedical studies during the last decade as a means of estimating treatment or covariate effects on survival. Advantages of RMST over the hazard ratio (HR) include increased interpretability and lack of reliance on the often tenuous proportional hazards assumption. Some authors have argued that RMST regression should generally be the frontline analysis as opposed to methods based on counting process increments. However, in order for the use of the RMST to be more mainstream, it is necessary to broaden the range of data structures to which pertinent methods can be applied. In this report, we address this issue from two angles. First, most of existing methodological development for directly modeling RMST has focused on multiplicative models. An additive model may be preferred due to goodness of fit and/or parameter interpretation. Second, many settings encountered nowadays feature high-dimensional categorical (nuisance) covariates, for which parameter estimation is best avoided. Motivated by these considerations, we propose stratified additive models for direct RMST analysis. The proposed methods feature additive covariate effects. Moreover, nuisance factors can be factored out of the estimation, akin to stratification in Cox regression, such that focus can be appropriately awarded to the parameters of chief interest. Large-sample properties of the proposed estimators are derived, and a simulation study is performed to assess finite-sample performance. In addition, we provide techniques for evaluating a fitted model with respect to risk discrimination and predictive accuracy. The proposed methods are then applied to liver transplant data to estimate the effects of donor characteristics on posttransplant survival time.

在过去的十年中,受限平均生存时间(RMST)分析在生物医学研究中越来越普遍,成为估计治疗或共变量对生存影响的一种手段。与危险比(HR)相比,RMST 的优点包括可解释性更强,而且不依赖于往往很脆弱的比例危险假设。一些学者认为,RMST 回归通常应作为一线分析方法,而不是基于计数过程增量的方法。然而,为了使 RMST 的使用更加主流化,有必要扩大可应用相关方法的数据结构的范围。在本报告中,我们将从两个角度探讨这一问题。首先,现有的直接建立 RMST 模型的方法大多集中在乘法模型上。出于拟合度和/或参数解释的考虑,加法模型可能更受欢迎。其次,目前遇到的许多情况都具有高维分类(滋扰)协变量,最好避免对其进行参数估计。基于这些考虑,我们提出了用于直接 RMST 分析的分层加法模型。所提出的方法具有协变量的加性效应。此外,干扰因素可以从估计中剔除,类似于 Cox 回归中的分层,这样就可以将重点适当地放在主要相关参数上。我们推导出了所建议估计器的大样本特性,并进行了模拟研究以评估有限样本性能。此外,我们还提供了评估拟合模型的风险判别和预测准确性的技术。然后将所提出的方法应用于肝脏移植数据,以估计捐赠者特征对移植后存活时间的影响。
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引用次数: 0
Sample Size Calculation Under Nonproportional Hazards Using Average Hazard Ratios 使用平均危险比计算非比例危险下的样本量。
IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-12 DOI: 10.1002/bimj.202300271
Ina Dormuth, Markus Pauly, Geraldine Rauch, Carolin Herrmann

Many clinical trials assess time-to-event endpoints. To describe the difference between groups in terms of time to event, we often employ hazard ratios. However, the hazard ratio is only informative in the case of proportional hazards (PHs) over time. There exist many other effect measures that do not require PHs. One of them is the average hazard ratio (AHR). Its core idea is to utilize a time-dependent weighting function that accounts for time variation. Though propagated in methodological research papers, the AHR is rarely used in practice. To facilitate its application, we unfold approaches for sample size calculation of an AHR test. We assess the reliability of the sample size calculation by extensive simulation studies covering various survival and censoring distributions with proportional as well as nonproportional hazards (N-PHs). The findings suggest that a simulation-based sample size calculation approach can be useful for designing clinical trials with N-PHs. Using the AHR can result in increased statistical power to detect differences between groups with more efficient sample sizes.

许多临床试验都会评估从时间到事件的终点。为了描述不同组别在事件发生时间上的差异,我们通常采用危险比。然而,危害比只有在时间比例危害(PHs)的情况下才有参考价值。还有许多其他不需要 PH 的效应测量方法。平均危险比(AHR)就是其中之一。其核心理念是利用随时间变化的加权函数来考虑时间变化。虽然 AHR 在方法论研究论文中广为传播,但在实践中却很少使用。为了便于应用,我们展开了 AHR 检验的样本量计算方法。我们通过广泛的模拟研究评估了样本量计算的可靠性,这些模拟研究涵盖了各种生存和剔除分布,以及比例和非比例危害(N-PHs)。研究结果表明,基于模拟的样本量计算方法可用于设计 N-PHs 临床试验。使用 AHR 可以提高统计能力,以更有效的样本量发现组间差异。
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引用次数: 0
Adaptive Multiple Comparisons With the Best 最佳自适应多重比较。
IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-10 DOI: 10.1002/bimj.202300242
Haoyu Chen, Werner Brannath, Andreas Futschik

Subset selection methods aim to choose a nonempty subset of populations including a best population with some prespecified probability. An example application involves location parameters that quantify yields in agriculture to select the best wheat variety. This is quite different from variable selection problems, for instance, in regression.

Unfortunately, subset selection methods can become very conservative when the parameter configuration is not least favorable. This will lead to a selection of many non-best populations, making the set of selected populations less informative. To solve this issue, we propose less conservative adaptive approaches based on estimating the number of best populations. We also discuss variants of our adaptive approaches that are applicable when the sample sizes and/or variances differ between populations. Using simulations, we show that our methods yield a desirable performance. As an illustration of potential gains, we apply them to two real datasets, one on the yield of wheat varieties and the other obtained via genome sequencing of repeated samples.

子集选择方法的目的是选择一个非空的种群子集,其中包括具有某种预设概率的最佳种群。一个应用实例涉及农业中量化产量的位置参数,以选择最佳的小麦品种。这与回归等变量选择问题截然不同。遗憾的是,当参数配置不是最有利时,子集选择方法会变得非常保守。这将导致选择许多非最佳种群,从而使所选种群集的信息量减少。为了解决这个问题,我们提出了基于估计最佳种群数量的不太保守的自适应方法。我们还讨论了适应性方法的变体,这些变体适用于样本大小和/或种群间方差不同的情况。通过模拟,我们证明我们的方法具有理想的性能。为了说明潜在的收益,我们将这些方法应用于两个真实数据集,一个是关于小麦品种产量的数据集,另一个是通过重复样本的基因组测序获得的数据集。
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引用次数: 0
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Biometrical Journal
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