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A case study: Assessing the efficacy of the revised dosage regimen via prediction model for recurrent event rate using biomarker data. 案例研究:利用生物标志物数据建立复发率预测模型,评估修订剂量方案的疗效。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-01 Epub Date: 2024-02-05 DOI: 10.1002/pst.2362
Ahrim Youn, Jiarui Chi, Yue Cui, Hui Quan

In recently conducted phase III trials in a rare disease area, patients received monthly treatment at a high dose of the drug, which targets to lower a specific biomarker level, closely associated with the efficacy endpoint, to around 10% across patients. Although this high dose demonstrated strong efficacy, treatments were withheld due to the reports of serious adverse events. Dosing in these studies were later resumed at a reduced dosage which targets to lower the biomarker level to 15%-35% across patients. Two questions arose after this disruption. The first is whether the efficacy of this revised regimen as measured by the reduction in annualized event rate is adequate to support the continuation of the development and the second is whether the potential bias due to the loss of patients during this dosing gap process can be gauged. To address these questions, we built a prediction model that quantitatively characterizes biomarker vs. endpoint relationship and predicts efficacy at the 15%-35% range of the biomarker level using the available data from the original high dose. This model predicts favorable event rate in the target biomarker level and shows that the bias due to the loss of patients is limited. These results support the continued development of the revised regimen, however, given the limitation of the data available, this prediction is planned to be validated further when data under the revised regimen become available.

最近在一个罕见病领域开展的 III 期试验中,患者每月接受一次高剂量药物治疗,目标是将与疗效终点密切相关的特定生物标志物水平降至患者的 10%左右。虽然这种高剂量药物显示出很强的疗效,但由于出现了严重的不良反应,治疗被迫中止。后来,这些研究恢复了减量给药,目标是将患者的生物标志物水平降至 15%-35%。这次中断后出现了两个问题。第一个问题是,根据年化事件发生率的降低程度来衡量,这一修订方案的疗效是否足以支持继续开发;第二个问题是,是否可以衡量在这一剂量间隙过程中因患者流失而产生的潜在偏差。为了解决这些问题,我们建立了一个预测模型,定量描述生物标志物与终点的关系,并利用原始高剂量的可用数据预测生物标志物水平在 15%-35% 范围内的疗效。该模型预测了目标生物标志物水平的有利事件发生率,并表明由于患者流失造成的偏差是有限的。这些结果支持继续开发修订后的治疗方案,但鉴于现有数据的局限性,计划在获得修订后治疗方案的数据后进一步验证这一预测。
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引用次数: 0
Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation. 预测新研究的亚组治疗效果:在制药公司开展数据挑战的动机、结果和经验。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-01 Epub Date: 2024-02-07 DOI: 10.1002/pst.2368
Björn Bornkamp, Silvia Zaoli, Michela Azzarito, Ruvie Martin, Carsten Philipp Müller, Conor Moloney, Giulia Capestro, David Ohlssen, Mark Baillie

We present the motivation, experience, and learnings from a data challenge conducted at a large pharmaceutical corporation on the topic of subgroup identification. The data challenge aimed at exploring approaches to subgroup identification for future clinical trials. To mimic a realistic setting, participants had access to 4 Phase III clinical trials to derive a subgroup and predict its treatment effect on a future study not accessible to challenge participants. A total of 30 teams registered for the challenge with around 100 participants, primarily from Biostatistics organization. We outline the motivation for running the challenge, the challenge rules, and logistics. Finally, we present the results of the challenge, the participant feedback as well as the learnings. We also present our view on the implications of the results on exploratory analyses related to treatment effect heterogeneity.

我们介绍了一家大型制药公司就亚组识别主题开展的数据挑战赛的动机、经验和教训。数据挑战旨在探索未来临床试验的亚组识别方法。为了模拟现实环境,参赛者可以访问 4 项 III 期临床试验,以得出一个亚组,并预测其对挑战者无法访问的未来研究的治疗效果。共有 30 个团队报名参加挑战赛,参赛者约 100 人,主要来自生物统计学组织。我们概述了举办挑战赛的动机、挑战赛规则和后勤工作。最后,我们介绍了挑战赛的结果、参赛者的反馈以及学习成果。我们还介绍了我们对与治疗效果异质性相关的探索性分析结果的影响的看法。
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引用次数: 0
Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence. 评估基于群体的轨迹建模方法在发现不同服药模式方面的性能。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-01 Epub Date: 2024-02-08 DOI: 10.1002/pst.2365
Awa Diop, Alind Gupta, Sabrina Mueller, Louis Dron, Ofir Harari, Heather Berringer, Vinusha Kalatharan, Jay J H Park, Miceline Mésidor, Denis Talbot

It is well known that medication adherence is critical to patient outcomes and can decrease patient mortality. The Pharmacy Quality Alliance (PQA) has recognized and identified medication adherence as an important indicator of medication-use quality. Hence, there is a need to use the right methods to assess medication adherence. The PQA has endorsed the proportion of days covered (PDC) as the primary method of measuring adherence. Although easy to calculate, the PDC has however several drawbacks as a method of measuring adherence. PDC is a deterministic approach that cannot capture the complexity of a dynamic phenomenon. Group-based trajectory modeling (GBTM) is increasingly proposed as an alternative to capture heterogeneity in medication adherence. The main goal of this paper is to demonstrate, through a simulation study, the ability of GBTM to capture treatment adherence when compared to its deterministic PDC analogue and to the nonparametric longitudinal K-means. A time-varying treatment was generated as a quadratic function of time, baseline, and time-varying covariates. Three trajectory models are considered combining a cat's cradle effect, and a rainbow effect. The performance of GBTM was compared to the PDC and longitudinal K-means using the absolute bias, the variance, the c-statistics, the relative bias, and the relative variance. For all explored scenarios, we find that GBTM performed better in capturing different patterns of medication adherence with lower relative bias and variance even under model misspecification than PDC and longitudinal K-means.

众所周知,坚持用药对患者的治疗效果至关重要,并能降低患者死亡率。药房质量联盟 (PQA) 已将用药依从性视为衡量用药质量的一项重要指标。因此,有必要使用正确的方法来评估用药依从性。PQA 已认可将覆盖天数比例 (PDC) 作为衡量用药依从性的主要方法。尽管 PDC 易于计算,但作为一种衡量用药依从性的方法,它也有一些缺点。PDC 是一种确定性方法,无法捕捉动态现象的复杂性。基于群体的轨迹建模(GBTM)被越来越多地提出,作为捕捉服药依从性异质性的替代方法。本文的主要目的是通过模拟研究,展示 GBTM 与确定性 PDC 类似方法和非参数纵向 K-means 相比,捕捉治疗依从性的能力。随时间变化的治疗方法是由时间、基线和随时间变化的协变量构成的二次函数。考虑了三种轨迹模型,包括猫的摇篮效应和彩虹效应。使用绝对偏差、方差、c 统计量、相对偏差和相对方差对 GBTM 的性能与 PDC 和纵向 K-means 进行了比较。我们发现,与 PDC 和纵向 K-means相比,GBTM 在所有探讨的情况下都能更好地捕捉不同的服药模式,即使在模型错配的情况下,其相对偏差和方差也较低。
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引用次数: 0
On sample size calculation in drug interaction trials. 关于药物相互作用试验的样本量计算。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-01 Epub Date: 2024-02-14 DOI: 10.1002/pst.2367
Paul Meyvisch, Mitra Ebrahimpoor

Drug-drug interaction (DDI) trials are an important part of drug development as they provide evidence on the benefits and risks when two or more drugs are taken concomitantly. Sample size calculation is typically recommended to be based on the existence of clinically justified no-effect boundaries but these are challenging to define in practice, while the default no-effect boundaries of 0.8-1.25 are known to be overly conservative requiring a large sample size. In addition, no-effect boundaries are of little use when there is prior pharmacological evidence that a mild or moderate interaction between two drugs may be present, in which case effect boundaries would be more useful. We introduce precision-based sample size calculation that accounts for both the stochastic nature of the pharmacokinetic parameters and the anticipated width of (no-)effect boundaries, should these exist. The methodology is straightforward, requires considerably less sample size and has favorable operating characteristics. A case study on statins is presented to illustrate the ideas.

药物相互作用(DDI)试验是药物开发的重要组成部分,因为它们提供了两种或两种以上药物同时服用时的益处和风险证据。样本量的计算通常建议以临床上合理的无效应界限为基础,但这些界限在实际操作中很难界定,而默认的 0.8-1.25 无效应界限又过于保守,需要较大的样本量。此外,当已有药理学证据表明两种药物之间可能存在轻度或中度相互作用时,无效应界限就没有什么用处了,在这种情况下,效应界限会更有用。我们引入了基于精确度的样本量计算方法,既考虑了药代动力学参数的随机性,又考虑了(无)效应界限(如果存在)的预期宽度。这种方法简单明了,所需的样本量少得多,而且具有良好的操作特性。本文通过一个关于他汀类药物的案例研究来说明这一观点。
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引用次数: 0
On the relative conservativeness of Bayesian logistic regression method in oncology dose-finding studies. 论贝叶斯逻辑回归法在肿瘤剂量测定研究中的相对保守性。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-01 Epub Date: 2024-02-05 DOI: 10.1002/pst.2364
Cheng-Han Yang, Guanghui Cheng, Ruitao Lin

The Bayesian logistic regression method (BLRM) is a widely adopted and flexible design for finding the maximum tolerated dose in oncology phase I studies. However, the BLRM design has been criticized in the literature for being overly conservative due to the use of the overdose control rule. Recently, a discussion paper titled "Improving the performance of Bayesian logistic regression model with overall control in oncology dose-finding studies" in Statistics in Medicine has proposed an overall control rule to address the "excessive conservativeness" of the standard BLRM design. In this short communication, we discuss the relative conservativeness of the standard BLRM design and also suggest a dose-switching rule to further enhance its performance.

贝叶斯逻辑回归法(BLRM)是在肿瘤学 I 期研究中寻找最大耐受剂量时广泛采用的一种灵活设计。然而,由于使用了超剂量控制规则,BLRM 设计在文献中被批评为过于保守。最近,《医学统计学》(Statistics in Medicine)杂志发表了一篇题为 "在肿瘤学剂量探索研究中提高带有总体控制的贝叶斯逻辑回归模型的性能 "的讨论文章,针对标准 BLRM 设计的 "过度保守性 "提出了一种总体控制规则。在这篇短文中,我们讨论了标准 BLRM 设计的相对保守性,并提出了进一步提高其性能的剂量切换规则。
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引用次数: 0
Transporting randomized trial results to estimate counterfactual survival functions in target populations. 传输随机试验结果,估算目标人群的反事实生存函数。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-01 Epub Date: 2024-01-17 DOI: 10.1002/pst.2354
Zhiqiang Cao, Youngjoo Cho, Fan Li

When the distributions of treatment effect modifiers differ between a randomized trial and an external target population, the sample average treatment effect in the trial may be substantially different from the target population average treatment, and accurate estimation of the latter requires adjusting for the differential distribution of effect modifiers. Despite the increasingly rich literature on transportability, little attention has been devoted to methods for transporting trial results to estimate counterfactual survival functions in target populations, when the primary outcome is time to event and subject to right censoring. In this article, we study inverse probability weighting and doubly robust estimators to estimate counterfactual survival functions and the target average survival treatment effect in the target population, and provide their respective approximate variance estimators. We focus on a common scenario where the target population information is observed only through a complex survey, and elucidate how the survey weights can be incorporated into each estimator we considered. Simulation studies are conducted to examine the finite-sample performances of the proposed estimators in terms of bias, efficiency and coverage, under both correct and incorrect model specifications. Finally, we apply the proposed method to assess transportability of the results in the Action to Control Cardiovascular Risk in Diabetes-Blood Pressure (ACCORD-BP) trial to all adults with Diabetes in the United States.

当随机试验和外部目标人群的治疗效果修饰因子分布不同时,试验中的样本平均治疗效果可能与目标人群的平均治疗效果大相径庭,而要准确估计后者,就需要对效果修饰因子的不同分布进行调整。尽管有关可迁移性的文献越来越丰富,但人们很少关注如何迁移试验结果,以估计目标人群中的反事实生存函数(当主要结果是事件发生时间并受右侧删减影响时)。在本文中,我们研究了反概率加权法和双重稳健估计法来估计目标人群中的反事实生存函数和目标平均生存治疗效果,并提供了各自的近似方差估计法。我们将重点放在仅通过复杂调查观测到目标人群信息的常见情景上,并阐明了如何将调查权重纳入我们所考虑的每种估计器中。我们还进行了模拟研究,以检验在正确和不正确的模型规格下,所提出的估计器在偏差、效率和覆盖率方面的有限样本性能。最后,我们将提出的方法用于评估控制糖尿病心血管风险-血压(ACCORD-BP)试验结果在美国所有成年糖尿病患者中的可移植性。
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引用次数: 0
Shrinkage priors for isotonic probability vectors and binary data modeling, with applications to dose-response modeling. 等效概率向量和二元数据建模的收缩先验,并应用于剂量反应建模。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-01 Epub Date: 2024-02-23 DOI: 10.1002/pst.2372
Philip S Boonstra, Daniel R Owen, Jian Kang

Motivated by the need to model dose-response or dose-toxicity curves in clinical trials, we develop a new horseshoe-based prior for Bayesian isotonic regression modeling a binary outcome against an ordered categorical predictor, where the probability of the outcome is assumed to be monotonically non-decreasing with the predictor. The set of differences between outcome probabilities in consecutive categories of the predictor is equipped with a multivariate prior having support over simplex. The Dirichlet distribution, which can be derived from a normalized sum of independent gamma-distributed random variables, is a natural choice of prior, but using mathematical and simulation-based arguments, we show that the resulting posterior is prone to underflow and other numerical instabilities, even under simple data configurations. We propose an alternative prior based on horseshoe-type shrinkage that is numerically more stable. We show that this horseshoe-based prior is not subject to the numerical instability seen in the Dirichlet/gamma-based prior and that the horseshoe-based posterior can estimate the underlying true curve more efficiently than the Dirichlet-based one. We demonstrate the use of this prior in a model predicting the occurrence of radiation-induced lung toxicity in lung cancer patients as a function of dose delivered to normal lung tissue. Our methodology is implemented in the R package isotonicBayes and therefore suitable for use in the design of dose-finding studies or other dose-response modeling contexts.

受临床试验中剂量-反应或剂量-毒性曲线建模需要的启发,我们开发了一种新的基于马蹄铁的贝叶斯等容回归先验,将二元结果与有序分类预测因子进行建模,其中假定结果概率随预测因子单调非递减。预测因子的连续类别中结果概率的差异集配备了一个多变量先验,该先验在单纯形上具有支持。Dirichlet 分布可以从独立伽马分布随机变量的归一化总和中导出,是先验值的自然选择,但通过数学和模拟论证,我们发现即使在简单的数据配置下,得到的后验值也容易出现下溢和其他数值不稳定性。我们提出了另一种基于马蹄型收缩的先验,在数值上更加稳定。我们证明,这种基于马蹄形的先验不会出现基于 Dirichlet/gamma 先验的数值不稳定性,而且基于马蹄形的后验比基于 Dirichlet 的后验能更有效地估计出潜在的真实曲线。我们在一个预测肺癌患者辐射诱发肺毒性的模型中演示了该先验值的使用,该模型是正常肺组织所受剂量的函数。我们的方法是在 R 软件包 isotonicBayes 中实现的,因此适用于剂量寻找研究或其他剂量反应建模的设计。
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引用次数: 0
The flaw of averages: Bayes factors as posterior means of the likelihood ratio. 平均值的缺陷:贝叶斯因子作为似然比的后验手段。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-01 Epub Date: 2024-01-28 DOI: 10.1002/pst.2355
Charles C Liu, Ron Xiaolong Yu, Murray Aitkin

As an alternative to the Frequentist p-value, the Bayes factor (or ratio of marginal likelihoods) has been regarded as one of the primary tools for Bayesian hypothesis testing. In recent years, several researchers have begun to re-analyze results from prominent medical journals, as well as from trials for FDA-approved drugs, to show that Bayes factors often give divergent conclusions from those of p-values. In this paper, we investigate the claim that Bayes factors are straightforward to interpret as directly quantifying the relative strength of evidence. In particular, we show that for nested hypotheses with consistent priors, the Bayes factor for the null over the alternative hypothesis is the posterior mean of the likelihood ratio. By re-analyzing 39 results previously published in the New England Journal of Medicine, we demonstrate how the posterior distribution of the likelihood ratio can be computed and visualized, providing useful information beyond the posterior mean alone.

贝叶斯因子(或边际似然比)作为频数法 p 值的替代方法,一直被视为贝叶斯假设检验的主要工具之一。近年来,一些研究人员开始重新分析著名医学期刊以及美国食品与药物管理局批准药物试验的结果,结果表明贝叶斯因子得出的结论往往与 p 值不同。在本文中,我们研究了贝叶斯系数可直接量化证据相对强度的说法。特别是,我们证明,对于具有一致先验的嵌套假设,零假设相对于备择假设的贝叶斯因子是似然比的后验平均值。通过重新分析之前发表在《新英格兰医学杂志》上的 39 项结果,我们展示了如何计算似然比的后验分布并将其可视化,从而提供了超越后验平均值的有用信息。
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引用次数: 0
A generalized Bayesian optimal interval design for dose optimization in immunotherapy. 用于免疫疗法剂量优化的广义贝叶斯最优区间设计。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-01 Epub Date: 2024-01-31 DOI: 10.1002/pst.2369
Qing Xia, Kentaro Takeda, Yusuke Yamaguchi, Jun Zhang

For novel immuno-oncology therapies, the primary purpose of a dose-finding trial is to identify an optimal dose (OD), defined as the tolerable dose having adequate efficacy and immune response under the unpredictable dose-outcome (toxicity, efficacy, and immune response) relationships. In addition, the multiple low or moderate-grade toxicities rather than dose-limiting toxicities (DLTs) and multiple levels of efficacy should be evaluated differently in dose-finding to determine true OD for developing novel immuno-oncology therapies. We proposed a generalized Bayesian optimal interval design for immunotherapy, simultaneously considering efficacy and toxicity grades and immune response outcomes. The proposed design, named gBOIN-ETI design, is model-assisted and easy to implement to develop immunotherapy efficiently. The operating characteristics of the gBOIN-ETI are compared with other dose-finding trial designs in oncology by simulation across various realistic settings. Our simulations show that the gBOIN-ETI design could outperform the other available approaches in terms of both the percentage of correct OD selection and the average number of patients allocated to the OD across various realistic trial settings.

对于新型免疫肿瘤疗法,剂量试验的主要目的是确定最佳剂量(OD),即在不可预测的剂量-结果(毒性、疗效和免疫反应)关系下,具有足够疗效和免疫反应的可耐受剂量。此外,在剂量寻找过程中,应对多种低度或中度毒性(而非剂量限制性毒性(DLT))和多级疗效进行不同的评估,以确定开发新型免疫肿瘤疗法的真正OD。我们为免疫疗法提出了一种广义贝叶斯最优区间设计,同时考虑疗效和毒性等级以及免疫反应结果。该设计被命名为 gBOIN-ETI 设计,由模型辅助,易于实施,可高效开发免疫疗法。我们通过模拟各种现实环境,将 gBOIN-ETI 的运行特点与肿瘤学领域的其他剂量试验设计进行了比较。我们的模拟结果表明,gBOIN-ETI 设计在各种实际试验环境中,无论是在正确选择 OD 的百分比方面,还是在分配给 OD 的患者平均人数方面,都优于其他现有方法。
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引用次数: 0
Investigating Stability in Subgroup Identification for Stratified Medicine. 研究分层医疗亚组识别的稳定性。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-06-25 DOI: 10.1002/pst.2409
G M Hair, T Jemielita, S Mt-Isa, P M Schnell, R Baumgartner

Subgroup analysis may be used to investigate treatment effect heterogeneity among subsets of the study population defined by baseline characteristics. Several methodologies have been proposed in recent years and with these, statistical issues such as multiplicity, complexity, and selection bias have been widely discussed. Some methods adjust for one or more of these issues; however, few of them discuss or consider the stability of the subgroup assignments. We propose exploring the stability of subgroups as a sensitivity analysis step for stratified medicine to assess the robustness of the identified subgroups besides identifying possible factors that may drive this instability. After applying Bayesian credible subgroups, a nonparametric bootstrap can be used to assess stability at subgroup-level and patient-level. Our findings illustrate that when the treatment effect is small or not so evident, patients are more likely to switch to different subgroups (jumpers) across bootstrap resamples. In contrast, when the treatment effect is large or extremely convincing, patients generally remain in the same subgroup. While the proposed subgroup stability method is illustrated through Bayesian credible subgroups method on time-to-event data, this general approach can be used with other subgroup identification methods and endpoints.

亚组分析可用于研究由基线特征定义的研究人群亚组之间的治疗效果异质性。近年来提出了几种方法,这些方法的统计问题,如多重性、复杂性和选择偏倚等已被广泛讨论。有些方法会对其中一个或多个问题进行调整,但很少有方法讨论或考虑亚组分配的稳定性。我们建议将探讨亚组的稳定性作为分层医疗的敏感性分析步骤,以评估所确定的亚组的稳健性,同时找出可能导致这种不稳定性的因素。在应用贝叶斯可信亚组后,可使用非参数引导法评估亚组和患者层面的稳定性。我们的研究结果表明,当治疗效果较小或不太明显时,患者更有可能在自引导重抽样中切换到不同的亚组(跳组)。相反,当治疗效果较大或极具说服力时,患者一般会留在同一亚组。虽然所提出的亚组稳定性方法是通过贝叶斯可信亚组法对时间到事件数据进行说明的,但这种通用方法也可用于其他亚组识别方法和终点。
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引用次数: 0
期刊
Pharmaceutical Statistics
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