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Rejoinder to the letter: “Standard and reference‐based conditional mean imputation: Regulators and trial statisticians be aware!” 回信:"基于标准和参照的条件均值估算:监管者和试验统计人员请注意!"
IF 1.5 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-04-17 DOI: 10.1002/pst.2374
Marcel Wolbers, Alessandro Noci, Paul Delmar, Sean Yiu, Jonathan W. Bartlett
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引用次数: 0
Comparing various Bayesian random‐effects models for pooling randomized controlled trials with rare events 比较各种贝叶斯随机效应模型,以汇总具有罕见事件的随机对照试验
IF 1.5 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-04-17 DOI: 10.1002/pst.2392
Minghong Yao, Yulong Jia, Fan Mei, Yuning Wang, Kang Zou, Ling Li, Xin Sun
The meta‐analysis of rare events presents unique methodological challenges owing to the small number of events. Bayesian methods are often used to combine rare events data to inform decision‐making, as they can incorporate prior information and handle studies with zero events without the need for continuity corrections. However, the comparative performances of different Bayesian models in pooling rare events data are not well understood. We conducted a simulation to compare the statistical properties of four parameterizations based on the binomial‐normal hierarchical model, using two different priors for the treatment effect: weakly informative prior (WIP) and non‐informative prior (NIP), pooling randomized controlled trials with rare events using the odds ratio metric. We also considered the beta‐binomial model proposed by Kuss and the random intercept and slope generalized linear mixed models. The simulation scenarios varied based on the treatment effect, sample size ratio between the treatment and control arms, and level of heterogeneity. Performance was evaluated using median bias, root mean square error, median width of 95% credible or confidence intervals, coverage, Type I error, and empirical power. Two reviews are used to illustrate these methods. The results demonstrate that the WIP outperforms the NIP within the same model structure. Among the compared models, the model that included the treatment effect parameter in the risk model for the control arm did not perform well. Our findings confirm that rare events meta‐analysis faces the challenge of being underpowered, highlighting the importance of reporting the power of results in empirical studies.
由于罕见事件的数量较少,对罕见事件进行荟萃分析在方法上面临独特的挑战。贝叶斯方法通常用于合并罕见事件数据,为决策提供信息,因为这种方法可以纳入先验信息,并处理零事件的研究,而无需进行连续性校正。然而,人们对不同贝叶斯模型在汇集罕见事件数据方面的比较性能还不甚了解。我们进行了一次模拟,比较了基于二叉-正态分层模型的四种参数化的统计特性,并使用了两种不同的治疗效果先验:弱信息先验(WIP)和非信息先验(NIP),使用几率比指标汇集罕见事件的随机对照试验。我们还考虑了库斯提出的贝塔二叉模型以及随机截距和斜率广义线性混合模型。模拟方案根据治疗效果、治疗组和对照组之间的样本量比例以及异质性程度而有所不同。使用中位偏差、均方根误差、95% 可信区间或置信区间的中位宽度、覆盖率、I 类误差和经验功率对性能进行评估。两篇综述对这些方法进行了说明。结果表明,在相同的模型结构中,WIP 优于 NIP。在比较的模型中,将治疗效果参数纳入对照组风险模型的模型表现不佳。我们的研究结果证实,罕见事件荟萃分析面临着幂次不足的挑战,这凸显了在实证研究中报告结果幂次的重要性。
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引用次数: 0
Comments on ‘standard and reference‐based conditional mean imputation’: Regulators and trial statisticians be aware! 关于 "基于标准和参照的条件均值估算 "的评论:监管者和试验统计人员须知!
IF 1.5 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-04-17 DOI: 10.1002/pst.2373
Suzie Cro, Tim P. Morris, James H. Roger, James R. Carpenter
Accurate frequentist performance of a method is desirable in confirmatory clinical trials, but is not sufficient on its own to justify the use of a missing data method. Reference‐based conditional mean imputation, with variance estimation justified solely by its frequentist performance, has the surprising and undesirable property that the estimated variance becomes smaller the greater the number of missing observations; as explained under jump‐to‐reference it effectively forces the true treatment effect to be exactly zero for patients with missing data.
在确证性临床试验中,一种方法的精确频数性能是可取的,但其本身并不足以证明使用缺失数据方法是合理的。基于参照的条件均值估算法的方差估计仅以其频数性能为依据,但却有一个令人惊讶且不可取的特性,即缺失观察指标越多,估计方差越小;正如 "跳至参照 "所解释的那样,它实际上迫使缺失数据患者的真实治疗效果恰好为零。
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引用次数: 0
Tree-temporal scan statistics for safety signal detection in vaccine clinical trials 用于疫苗临床试验安全信号检测的树状时序扫描统计
IF 1.5 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-04-15 DOI: 10.1002/pst.2391
François Haguinet, Fabian Tibaldi, Christophe Dessart, Andrew Bate
The evaluation of safety is critical in all clinical trials. However, the quantitative analysis of safety data in clinical trials poses statistical difficulties because of multiple potentially overlapping endpoints. Tree-temporal scan statistic approaches address this issue and have been widely employed in other data sources, but not to date in clinical trials. We evaluated the performance of three complementary scan statistical methods for routine quantitative safety signal detection: the self-controlled tree-temporal scan (SCTTS), a tree-temporal scan based on group comparison (BGTTS), and a log-rank based tree-temporal scan (LgRTTS). Each method was evaluated using data from two phase III clinical trials, and simulated data (simulation study). In the case study, the reference set was adverse events (AEs) in the Reference Safety Information of the evaluated vaccine. The SCTTS method had higher sensitivity than other methods, and after dose 1 detected 80 true positives (TP) with a positive predictive value (PPV) of 60%. The LgRTTS detected 49 TPs with 69% PPV. The BGTTS had 90% of PPV with 38 TPs. In the simulation study, with simulated reference sets of AEs, the SCTTS method had good sensitivity to detect transient effects. The LgRTTS method showed the best performance for the detection of persistent effects, with high sensitivity and expected probability of type I error. These three methods provide complementary approaches to safety signal detection in clinical trials or across clinical development programmes. All three methods formally adjust for multiple testing of large numbers of overlapping endpoints without being excessively conservative.
安全性评估在所有临床试验中都至关重要。然而,由于存在多个可能重叠的终点,临床试验中安全性数据的定量分析在统计学上存在困难。树状时空扫描统计方法可以解决这一问题,并已广泛应用于其他数据源,但迄今为止尚未应用于临床试验。我们评估了用于常规定量安全信号检测的三种互补扫描统计方法的性能:自控树状时空扫描(SCTTS)、基于组间比较的树状时空扫描(BGTTS)和基于对数秩的树状时空扫描(LgRTTS)。每种方法都使用两项 III 期临床试验的数据和模拟数据(模拟研究)进行了评估。在案例研究中,参考集是被评估疫苗的参考安全信息中的不良事件(AEs)。SCTTS 方法的灵敏度高于其他方法,在剂量 1 后检测出 80 个真阳性 (TP),阳性预测值 (PPV) 为 60%。LgRTTS 检测出 49 个 TP,PPV 为 69%。BGTTS 检测出 38 个 TP,PPV 为 90%。在模拟研究中,利用模拟的 AEs 参考集,SCTTS 方法在检测瞬时效应方面具有良好的灵敏度。LgRTTS 方法在检测持续效应方面表现最佳,灵敏度高,预期 I 型错误概率也高。这三种方法为临床试验或整个临床开发计划中的安全信号检测提供了互补方法。这三种方法都能对大量重叠终点的多重测试进行正式调整,而不会过于保守。
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引用次数: 0
Mathematical programming tools for randomization purposes in small two‐arm clinical trials: A case study with real data 用于小型双臂临床试验随机化的数学编程工具:真实数据案例研究
IF 1.5 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-04-13 DOI: 10.1002/pst.2388
Alan R. Vazquez, Weng‐Kee Wong
Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate‐adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.
临床试验中的现代随机化方法无一例外都是自适应的,也就是说,在将下一个受试者分配到治疗组时,会使用试验中积累的信息。最近的一些自适应随机化方法使用数学编程来构建有吸引力的临床试验,以平衡治疗组的特征,如治疗组的规模和受试者的协变量分布。我们回顾了其中一些方法,并将它们的性能与小型临床试验中常见的协变量自适应随机化方法进行了比较。我们引入了一种能量距离测量方法,利用受试者协变量的联合分布来比较两组之间的差异。与使用受试者的边际协变量分布来评估两组之间的差异相比,这种度量方法更具吸引力。通过数值实验,我们证明了数学编程方法在新指标下的优势。在补充材料中,我们提供了 R 代码来重现我们的研究结果,并方便比较不同的随机化程序。
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引用次数: 0
Predictive Ppk calculations for biologics and vaccines using a Bayesian approach – a tutorial 使用贝叶斯方法对生物制剂和疫苗进行预测性 Ppk 计算--教程
IF 1.5 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-04-11 DOI: 10.1002/pst.2380
Jos Weusten, Jianfang Hu
In pharmaceutical manufacturing, especially biologics and vaccines manufacturing, emphasis on speedy process development can lead to inadequate process development, which often results in less robust commercial manufacturing process after launch. Process performance index (Ppk) is a statistical measurement of the ability of a process to produce output within specification limits over a period of time. In biopharmaceutical manufacturing, progression in process development is based on Critical Quality Attributes meeting their specification limits, lacking insight into the process robustness. Ppk is typically estimated after 15–30 commercial batches at which point it may be too late/too complex to make process adjustments to enhance robustness. The use of Bayesian statistics, prior knowledge, and input from Subject matter experts (SMEs) offers an opportunity to make predictions on process capability during the development cycle. Developing a standard methodology to assess long term process capability at various stages of development provides several benefits: provides opportunity for early insight into process vulnerabilities thereby enabling resolution pre‐licensure; identifies area of the process to prioritize and focus on during process development/process characterization (PC) using a data‐driven approach; and ultimately results in higher process robustness/process knowledge at launch. We propose a Bayesian‐based method to predict the performance of a manufacturing process at full manufacturing scale during the development and commercialization phase, before commercial data exists. Under Bayesian framework, limited development data for the process of interest at hand, data from similar products, general SME knowledge, and literature can be carefully formulated into informative priors. The implementation of the proposed approach is presented through two examples. To allow for continuous improvement during process development, we recommend to embed this approach of using predictive Ppk at pre‐defined commercialization stage‐gates, for example, at completion of process development, prior to and completion of PC, prior to technology transfer runs (Engineering/Process Performance Qualification, PPQ), and prior to commercial specification setting.
在制药行业,尤其是生物制剂和疫苗制造行业,对快速工艺开发的重视可能会导致工艺开发不充分,这往往会导致上市后的商业制造工艺不够稳健。工艺性能指数(Ppk)是对工艺在一段时间内按照规格限制生产产出的能力进行的统计测量。在生物制药生产中,工艺开发的进展基于关键质量属性是否满足其规格限制,而缺乏对工艺稳健性的深入了解。Ppk 通常在 15-30 个商业批次后进行估算,此时再进行工艺调整以提高稳健性可能为时已晚/过于复杂。贝叶斯统计法、先验知识和主题专家 (SME) 的意见为在开发周期内预测工艺能力提供了机会。开发一种标准方法来评估不同开发阶段的长期工艺能力有以下几个好处:提供早期洞察工艺漏洞的机会,从而能够在认证前解决问题;使用数据驱动方法确定工艺开发/工艺特征描述 (PC) 期间需要优先考虑和重点关注的工艺领域;以及最终在投产时获得更高的工艺稳健性/工艺知识。我们提出了一种基于贝叶斯的方法,用于在商业数据存在之前,在开发和商业化阶段预测制造工艺在全制造规模下的性能。在贝叶斯框架下,手头相关工艺的有限开发数据、类似产品的数据、中小企业的一般知识以及文献资料都可以被精心编制成信息丰富的前置条件。本文通过两个例子介绍了所建议方法的实施。为了在工艺开发过程中实现持续改进,我们建议在预先确定的商业化阶段,例如在工艺开发完成时、PC 完成之前、技术转让运行(工程/工艺性能鉴定,PPQ)之前以及商业规格制定之前,采用这种使用预测 Ppk 的方法。
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引用次数: 0
Dynamic borrowing of historical controls adjusting for covariates in vaccine efficacy clinical trials 在疫苗疗效临床试验中动态借用历史对照组调整协变量
IF 1.5 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-04-09 DOI: 10.1002/pst.2384
Andrea Callegaro, Yongyi Luo, Naveen Karkada, Toufik Zahaf
Traditional vaccine efficacy trials usually use fixed designs and often require large sample sizes. Recruiting a large number of subjects can make the trial expensive, long, and difficult to conduct. A possible approach to reduce the sample size and speed up the development is to use historical controls. In this paper, we extend the robust mixture prior (RMP) approach (a well established approach for Bayesian dynamic borrowing of historical controls) to adjust for covariates. The adjustment is done using classical methods from causal inference: inverse probability of treatment weighting, G‐computation and double‐robust estimation. We evaluate these covariate‐adjusted RMP approaches using a comprehensive simulation study and demonstrate their use by performing a retrospective analysis of a prophylactic human papillomavirus vaccine efficacy trial. Adjusting for covariates reduces the drift between current and historical controls, with a beneficial effect on bias, control of type I error and power.
传统的疫苗疗效试验通常采用固定设计,通常需要大量样本。招募大量受试者会使试验变得昂贵、漫长且难以进行。减少样本量并加快研发速度的一种可行方法是使用历史对照。在本文中,我们扩展了稳健混合先验(RMP)方法(一种成熟的贝叶斯动态借用历史对照的方法),以调整协变量。调整使用的是因果推断的经典方法:逆概率处理加权、G 计算和双稳健估计。我们通过一项综合模拟研究对这些协变量调整后的 RMP 方法进行了评估,并通过对预防性人类乳头瘤病毒疫苗疗效试验进行回顾性分析来展示这些方法的应用。对协变量的调整减少了当前对照和历史对照之间的漂移,对偏差、I 型误差控制和功率都有好处。
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引用次数: 0
Information‐based group sequential design for post‐market safety monitoring of medical products using real world data 利用真实世界的数据,为医疗产品上市后安全监测进行基于信息的分组顺序设计
IF 1.5 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-04-08 DOI: 10.1002/pst.2385
Zhiwei Zhang, Carrie Nielson, Ching‐Yi Chuo, Zhishen Ye
Real world healthcare data are commonly used in post‐market safety monitoring studies to address potential safety issues related to newly approved medical products. Such studies typically involve repeated evaluations of accumulating safety data with respect to pre‐defined hypotheses, for which the group sequential design provides a rigorous and flexible statistical framework. A major challenge in designing a group sequential safety monitoring study is the uncertainty associated with product uptake, which makes it difficult to specify the final sample size or maximum duration of the study. To deal with this challenge, we propose an information‐based group sequential design which specifies a target amount of information that would produce adequate power for detecting a clinically significant effect size. At each interim analysis, the variance estimate for the treatment effect of interest is used to compute the current information time, and a pre‐specified alpha spending function is used to determine the stopping boundary. The proposed design can be applied to regression models that adjust for potential confounders and/or heterogeneous treatment exposure. Simulation results demonstrate that the proposed design performs reasonably well in realistic settings
真实世界的医疗保健数据通常用于上市后安全监测研究,以解决与新批准的医疗产品相关的潜在安全问题。此类研究通常涉及根据预先确定的假设对不断积累的安全性数据进行重复评估,而分组序列设计则为此类研究提供了严格而灵活的统计框架。设计分组序列安全性监测研究的一个主要挑战是与产品吸收相关的不确定性,这使得最终样本量或研究的最长持续时间难以确定。为了应对这一挑战,我们提出了一种基于信息的分组序列设计,该设计规定了一个目标信息量,该信息量将产生足够的功率来检测具有临床意义的效应大小。在每次中期分析中,相关治疗效果的方差估计值用于计算当前的信息时间,而预先指定的阿尔法支出函数用于确定停止边界。建议的设计可用于调整潜在混杂因素和/或异质性治疗暴露的回归模型。模拟结果表明,所提出的设计在现实环境中表现相当出色
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引用次数: 0
Balance diagnostics in propensity score analysis following multiple imputation: A new method 多重归因后倾向得分分析中的平衡诊断:一种新方法
IF 1.5 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-04-06 DOI: 10.1002/pst.2389
Sevinc Puren Yucel Karakaya, Ilker Unal
The combination of propensity score analysis and multiple imputation has been prominent in epidemiological research in recent years. However, studies on the evaluation of balance in this combination are limited. In this paper, we propose a new method for assessing balance in propensity score analysis following multiple imputation. A simulation study was conducted to evaluate the performance of balance assessment methods (Leyrat's, Leite's, and new method). Simulated scenarios varied regarding the presence of missing data in the control or treatment and control group, and the imputation model with/without outcome. Leyrat's method was more biased in all the studied scenarios. Leite's method and the combine method yielded balanced results with lower mean absolute difference, regardless of whether the outcome was included in the imputation model or not. Leyrat's method had a higher false positive ratio and Leite's and combine method had higher specificity and accuracy, especially when the outcome was not included in the imputation model. According to simulation results, most of time, Leyrat's method and Leite's method contradict with each other on appraising the balance. This discrepancy can be solved using new combine method.
近年来,倾向得分分析与多重估算的结合在流行病学研究中十分突出。然而,对这种组合的平衡性评估研究却很有限。在本文中,我们提出了一种在多重归因后评估倾向评分分析平衡性的新方法。我们进行了一项模拟研究,以评估平衡评估方法(Leyrat's、Leite's 和新方法)的性能。模拟情景因对照组或治疗组和对照组是否存在缺失数据以及有/无结果的估算模型而异。在所有研究场景中,Leyrat 方法的偏差更大。无论结果是否包含在估算模型中,莱特法和合并法的结果都比较均衡,平均绝对差值较低。Leyrat 方法的假阳性率较高,而 Leite 方法和组合方法的特异性和准确性较高,尤其是当结果未纳入估算模型时。根据模拟结果,在大多数情况下,Leyrat 方法和 Leite 方法在评估平衡方面相互矛盾。这种矛盾可以通过新的组合方法来解决。
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引用次数: 0
A practical guide to the appropriate analysis of eGFR data over time: A simulation study 对随时间变化的 eGFR 数据进行适当分析的实用指南:模拟研究
IF 1.5 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-04-03 DOI: 10.1002/pst.2381
Todd DeVries, Kevin J. Carroll, Sandra A. Lewis
In several therapeutic areas, including chronic kidney disease (CKD) and immunoglobulin A nephropathy (IgAN), there is a growing interest in how best to analyze estimated glomerular filtration rate (eGFR) data over time in randomized clinical trials including how to best accommodate situations where the rate of change is not anticipated to be linear over time, often due to possible short term hemodynamic effects of certain classes of interventions. In such situations, concerns have been expressed by regulatory authorities that the common application of single slope analysis models may induce Type I error inflation. This article aims to offer practical advice and guidance, including SAS codes, on the statistical methodology to be employed in an eGFR rate of change analysis and offers guidance on trial design considerations for eGFR endpoints. A two‐slope statistical model for eGFR data over time is proposed allowing for an analysis to simultaneously evaluate short term acute effects and long term chronic effects. A simulation study was conducted under a range of credible null and alternative hypotheses to evaluate the performance of the two‐slope model in comparison to commonly used single slope random coefficients models as well as to non‐slope based analyses of change from baseline or time normalized area under the curve (TAUC). Importantly, and contrary to preexisting concerns, these simulations demonstrate the absence of alpha inflation associated with the use of single or two‐slope random coefficient models, even when such models are misspecified, and highlight that any concern regarding model misspecification relates to power and not to lack of Type I error control.
在一些治疗领域,包括慢性肾脏病 (CKD) 和免疫球蛋白 A 肾病 (IgAN),人们越来越关注如何在随机临床试验中以最佳方式分析随时间变化的肾小球滤过率 (eGFR) 估计数据,包括如何以最佳方式适应随时间变化的速率不是线性的情况,这通常是由于某些类别的干预措施可能会产生短期血液动力学效应。在这种情况下,监管机构表示担心普遍应用单一斜率分析模型可能会引起 I 类错误膨胀。本文旨在就 eGFR 变化率分析中应采用的统计方法提供实用建议和指导(包括 SAS 代码),并就 eGFR 终点的试验设计注意事项提供指导。本文提出了一种随时间变化的 eGFR 数据双斜率统计模型,允许同时评估短期急性效应和长期慢性效应的分析。在一系列可信的零假设和替代假设下进行了模拟研究,以评估双斜率模型与常用的单斜率随机系数模型以及非斜率基线变化分析或时间归一化曲线下面积(TAUC)相比的性能。重要的是,与之前存在的担忧相反,这些模拟结果表明,使用单斜率或双斜率随机系数模型,即使这些模型被错误地指定,也不会出现α膨胀,并强调任何有关模型指定错误的担忧都与功率有关,而不是与缺乏I类错误控制有关。
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引用次数: 0
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Pharmaceutical Statistics
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