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A Personalized Dose-Finding Algorithm Based on Adaptive Gaussian Process Regression. 基于自适应高斯过程回归的个性化剂量确定算法
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-08-09 DOI: 10.1002/pst.2417
Yeonhee Park, Won Chang

Dose-finding studies play a crucial role in drug development by identifying the optimal dose(s) for later studies while considering tolerability. This not only saves time and effort in proceeding with Phase III trials but also improves efficacy. In an era of precision medicine, it is not ideal to assume patient homogeneity in dose-finding studies as patients may respond differently to the drug. To address this, we propose a personalized dose-finding algorithm that assigns patients to individualized optimal biological doses. Our design follows a two-stage approach. Initially, patients are enrolled under broad eligibility criteria. Based on the Stage 1 data, we fit a regression model of toxicity and efficacy outcomes on dose and biomarkers to characterize treatment-sensitive patients. In the second stage, we restrict the trial population to sensitive patients, apply a personalized dose allocation algorithm, and choose the recommended dose at the end of the trial. Simulation study shows that the proposed design reliably enriches the trial population, minimizes the number of failures, and yields superior operating characteristics compared to several existing dose-finding designs in terms of both the percentage of correct selection and the number of patients treated at target dose(s).

剂量摸底研究在药物研发中发挥着至关重要的作用,它可以在考虑耐受性的同时,为后续研究确定最佳剂量。这不仅能节省进行 III 期试验的时间和精力,还能提高疗效。在精准医疗时代,在剂量探索研究中假定患者具有同质性并不理想,因为患者可能对药物产生不同的反应。为了解决这个问题,我们提出了一种个性化剂量寻找算法,为患者分配个性化的最佳生物剂量。我们的设计采用两阶段方法。首先,根据广泛的资格标准招募患者。在第一阶段数据的基础上,我们根据剂量和生物标志物拟合了毒性和疗效结果的回归模型,以确定对治疗敏感的患者的特征。在第二阶段,我们将试验人群限定为敏感患者,应用个性化剂量分配算法,并在试验结束时选择推荐剂量。模拟研究表明,与现有的几种剂量寻找设计相比,所提出的设计能可靠地丰富试验人群,最大限度地减少失败次数,并在正确选择的百分比和按目标剂量治疗的患者人数方面产生更优越的操作特性。
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引用次数: 0
Applying the Estimand Framework to Non‐Inferiority Trials. 将 Estimand 框架应用于非劣效性试验。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-08-08 DOI: 10.1002/pst.2433
Helle Lynggaard, Oliver N Keene, Tobias Mütze, Sunita Rehal

Most published applications of the estimand framework have focused on superiority trials. However, non-inferiority trials present specific challenges compared to superiority trials. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use notes in their addendum on estimands and sensitivity analysis in clinical trials that there may be special considerations to the implementation of estimands in clinical trials with a non-inferiority objective yet provides little guidance. This paper discusses considerations that trial teams should make when defining estimands for a clinical trial with a non-inferiority objective. We discuss how the pre-addendum way of establishing non-inferiority can be embraced by the estimand framework including a discussion of the role of the Per Protocol analysis set. We examine what clinical questions of interest can be formulated in the context of non-inferiority trials and outline why we do not think it is sensible to describe an estimand as 'conservative'. The impact of the estimand framework on key considerations in non-inferiority trials such as whether trials should have more than one primary estimand, the choice of non-inferiority margin, assay sensitivity, switching from non-inferiority to superiority and estimation are discussed. We conclude by providing a list of recommendations, and important considerations for defining estimands for trials with a non-inferiority objective.

大多数已发表的估算值框架应用都集中在优效试验上。然而,与优效试验相比,非劣效试验面临着特殊的挑战。国际人用药品技术要求协调委员会在其关于临床试验中的估计指标和敏感性分析的增编中指出,在以非劣效性为目标的临床试验中实施估计指标时可能会有一些特殊的考虑因素,但几乎没有提供任何指导。本文讨论了试验团队在为具有非劣效性目标的临床试验定义估计指标时应注意的事项。我们讨论了估算指标框架如何采用增补前方式确定非劣效性,包括讨论每项协议分析集的作用。我们研究了在非劣效性试验中可以提出哪些临床问题,并概述了为什么我们认为将估计值描述为 "保守 "是不明智的。我们还讨论了估计指标框架对非劣效性试验中主要考虑因素的影响,如试验是否应该有一个以上的主要估计指标、非劣效边际的选择、检测灵敏度、从非劣效到优效的转换以及估计。最后,我们提供了一份建议清单,以及在以非劣效性为目标的试验中定义估计指标的重要注意事项。
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引用次数: 0
Tutorial on Firth's Logistic Regression Models for Biomarkers in Preclinical Space. 临床前生物标记物的 Firth Logistic 回归模型教程。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-08-06 DOI: 10.1002/pst.2422
Gina D'Angelo, Di Ran

Preclinical studies are broad and can encompass cellular research, animal trials, and small human trials. Preclinical studies tend to be exploratory and have smaller datasets that often consist of biomarker data. Logistic regression is typically the model of choice for modeling a binary outcome with explanatory variables such as genetic, imaging, and clinical data. Small preclinical studies can have challenging data that may include a complete separation or quasi-complete separation issue that will result in logistic regression inflated coefficient estimates and standard errors. Penalized regression approaches such as Firth's logistic regression are a solution to reduce the bias in the estimates. In this tutorial, a number of examples with separation (complete or quasi-complete) are illustrated and the results from both logistic regression and Firth's logistic regression are compared to demonstrate the inflated estimates from the standard logistic regression model and bias-reduction of the estimates from the penalized Firth's approach. R code and datasets are provided in the supplement.

临床前研究的范围很广,可以包括细胞研究、动物试验和小型人体试验。临床前研究往往是探索性的,数据集较小,通常由生物标记物数据组成。逻辑回归通常是二元结果建模的首选模型,其解释变量包括基因、成像和临床数据。小型临床前研究的数据可能具有挑战性,其中可能包括完全分离或准完全分离问题,这将导致逻辑回归膨胀的系数估计值和标准误差。Firth逻辑回归等惩罚回归方法是减少估计值偏差的一种解决方案。本教程将举例说明一些分离(完全或准完全)的例子,并对逻辑回归和 Firth 逻辑回归的结果进行比较,以展示标准逻辑回归模型的估计值膨胀和 Firth 惩罚回归方法的估计值偏差减小。附录中提供了 R 代码和数据集。
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引用次数: 0
Mixture Experimentation in Pharmaceutical Formulations: A Tutorial. 药物制剂中的混合物实验:教程。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-08-05 DOI: 10.1002/pst.2426
Lynne B Hare, Stan Altan, Hans Coppenolle

Mixture experimentation is commonly seen in pharmaceutical formulation studies, where the relative proportions of the individual components are modeled for effects on product attributes. The requirement that the sum of the component proportions equals 1 has given rise to the class of designs, known as mixture designs. The first mixture designs were published by Quenouille in 1953 but it took nearly 40 years for the earliest mixture design applications to be published in the pharmaceutical sciences literature by Kettaneh-Wold in 1991 and Waaler in 1992. Since then, the advent of efficient computer algorithms to generate designs has made this class of designs easily accessible to pharmaceutical statisticians, although the use of these designs appears to be an underutilized experimental strategy even today. One goal of this tutorial is to draw the attention of experimental statisticians to this class of designs and their advantages in pursuing formulation studies such as excipient compatibility studies. We present sufficient materials to introduce the novice practitioner to this class of design, associated models, and analysis strategies. An example of a mixture-process variable design is given as a case study.

混合物实验常见于药物制剂研究中,通过模拟单个成分的相对比例来确定对产品属性的影响。各组分比例之和等于 1 的要求产生了一类设计,即混合物设计。最早的混合设计由 Quenouille 于 1953 年发表,但过了近 40 年,Kettaneh-Wold 和 Waaler 才分别于 1991 年和 1992 年在制药科学文献中发表了最早的混合设计应用。从那时起,生成设计的高效计算机算法的出现使这一类设计很容易为制药统计学家所使用,尽管时至今日,使用这些设计似乎仍是一种未得到充分利用的实验策略。本教程的目的之一是提请实验统计人员注意这类设计及其在辅料相容性研究等制剂研究中的优势。我们将提供足够的材料,向新手介绍这类设计、相关模型和分析策略。我们还给出了一个混合过程变量设计的案例。
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引用次数: 0
Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off-Treatment Sequential Multiple Imputation. 使用非治疗序列多重估算法估算连续结果的治疗政策估计值。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-08-04 DOI: 10.1002/pst.2411
Thomas Drury, Juan J Abellan, Nicky Best, Ian R White

The estimands framework outlined in ICH E9 (R1) describes the components needed to precisely define the effects to be estimated in clinical trials, which includes how post-baseline 'intercurrent' events (IEs) are to be handled. In late-stage clinical trials, it is common to handle IEs like 'treatment discontinuation' using the treatment policy strategy and target the treatment effect on outcomes regardless of treatment discontinuation. For continuous repeated measures, this type of effect is often estimated using all observed data before and after discontinuation using either a mixed model for repeated measures (MMRM) or multiple imputation (MI) to handle any missing data. In basic form, both these estimation methods ignore treatment discontinuation in the analysis and therefore may be biased if there are differences in patient outcomes after treatment discontinuation compared with patients still assigned to treatment, and missing data being more common for patients who have discontinued treatment. We therefore propose and evaluate a set of MI models that can accommodate differences between outcomes before and after treatment discontinuation. The models are evaluated in the context of planning a Phase 3 trial for a respiratory disease. We show that analyses ignoring treatment discontinuation can introduce substantial bias and can sometimes underestimate variability. We also show that some of the MI models proposed can successfully correct the bias, but inevitably lead to increases in variance. We conclude that some of the proposed MI models are preferable to the traditional analysis ignoring treatment discontinuation, but the precise choice of MI model will likely depend on the trial design, disease of interest and amount of observed and missing data following treatment discontinuation.

ICH E9 (R1)中概述的估计因素框架描述了在临床试验中精确定义估计效应所需的组成部分,其中包括如何处理基线后 "并发 "事件(IEs)。在后期临床试验中,通常会使用治疗策略来处理 "治疗中断 "等 IEs,并针对治疗对结果的影响,而不管治疗中断与否。对于连续重复测量,通常使用重复测量混合模型(MMRM)或多重估算(MI)来处理任何缺失数据,并使用终止治疗前后的所有观察数据来估算这类效应。在基本形式上,这两种估算方法在分析中都忽略了治疗的中断,因此,如果患者中断治疗后的结果与仍在接受治疗的患者相比存在差异,而且中断治疗的患者缺失数据更常见,那么这两种方法就可能存在偏差。因此,我们提出并评估了一组 MI 模型,这些模型可以考虑治疗中断前后的结果差异。我们在规划一项呼吸系统疾病的 3 期试验时对这些模型进行了评估。我们发现,忽略治疗中断的分析会带来很大的偏差,有时还会低估变异性。我们还表明,提出的一些多元智能模型可以成功纠正偏差,但不可避免地会导致变异性增加。我们的结论是,一些建议的 MI 模型优于忽略治疗中断的传统分析,但 MI 模型的准确选择可能取决于试验设计、感兴趣的疾病以及治疗中断后的观察数据和缺失数据的数量。
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引用次数: 0
Efficient Study Design and Analysis of Longitudinal Dose-Response Data Using Fractional Polynomials. 利用分数多项式进行高效研究设计和纵向剂量反应数据分析
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-28 DOI: 10.1002/pst.2425
Benjamin F Hartley, Dave Lunn, Adrian P Mander

Correctly characterising the dose-response relationship and taking the correct dose forward for further study is a critical part of the drug development process. We use optimal design theory to compare different designs and show that using longitudinal data from all available timepoints in a continuous-time dose-response model can substantially increase the efficiency of estimation of the dose-response compared to a single timepoint model. We give theoretical results to calculate the efficiency gains for a large class of these models. For example, a linearly growing Emax dose-response in a population with a between/within-patient variance ratio ranging from 0.1 to 1 measured at six visits can be estimated with between 1.43 and 2.22 times relative efficiency gain, or equivalently, with 30% to a 55% reduced sample size, compared to a single model of the final timepoint. Fractional polynomials are a flexible way to incorporate data from repeated measurements, increasing precision without imposing strong constraints. Longitudinal dose-response models using two fractional polynomial terms are robust to mis-specification of the true longitudinal process while maintaining, often large, efficiency gains. These models have applications for characterising the dose-response at interim or final analyses.

正确描述剂量-反应关系并采取正确的剂量进行进一步研究是药物开发过程的关键部分。我们利用最优设计理论来比较不同的设计,结果表明,与单时间点模型相比,在连续时间剂量-反应模型中使用所有可用时间点的纵向数据可以大大提高剂量-反应的估算效率。我们给出了计算一大类此类模型效率提高的理论结果。例如,与最后一个时间点的单一模型相比,在一个患者间/患者内变异比在 0.1 到 1 之间的人群中,通过六次就诊测量的线性增长的 Emax 剂量反应的估计效率相对提高了 1.43 到 2.22 倍,或者说样本量减少了 30% 到 55%。分数多项式是纳入重复测量数据的一种灵活方法,可在不强加限制的情况下提高精确度。使用两个分数多项式项的纵向剂量-反应模型对真实纵向过程的错误规范具有很强的鲁棒性,同时还能保持较高的效率。这些模型可用于描述中期或最终分析的剂量反应特征。
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引用次数: 0
Strategy for Designing In Vivo Dose-Response Comparison Studies. 设计体内剂量-反应比较研究的策略
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-17 DOI: 10.1002/pst.2421
Steven Novick, Tianhui Zhang

In preclinical drug discovery, at the step of lead optimization of a compound, in vivo experimentation can differentiate several compounds in terms of efficacy and potency in a biological system of whole living organisms. For the lead optimization study, it may be desirable to implement a dose-response design so that compound comparisons can be made from nonlinear curves fitted to the data. A dose-response design requires more thought relative to a simpler study design, needing parameters for the number of doses, the dose values, and the sample size per dose. This tutorial illustrates how to calculate statistical power, choose doses, and determine sample size per dose for a comparison of two or more dose-response curves for a future in vivo study.

在临床前药物发现中,在化合物的先导优化步骤中,体内实验可以区分几种化合物在整个生物体的生物系统中的疗效和效力。在先导优化研究中,最好采用剂量-反应设计,这样就可以通过与数据拟合的非线性曲线对化合物进行比较。与简单的研究设计相比,剂量反应设计需要更多的考虑,需要剂量数、剂量值和每个剂量的样本量等参数。本教程说明了如何计算统计功率、选择剂量以及确定每个剂量的样本量,以便在未来的体内研究中比较两个或多个剂量-反应曲线。
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引用次数: 0
Reparametrized Firth's Logistic Regressions for Dose-Finding Study With the Biased-Coin Design. 采用偏币设计的剂量寻找研究中的重拟合 Firth Logistic 回归。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-16 DOI: 10.1002/pst.2423
Hyungwoo Kim, Seungpil Jung, Yudi Pawitan, Woojoo Lee

Finding an adequate dose of the drug by revealing the dose-response relationship is very crucial and a challenging problem in the clinical development. The main concerns in dose-finding study are to identify a minimum effective dose (MED) in anesthesia studies and maximum tolerated dose (MTD) in oncology clinical trials. For the estimation of MED and MTD, we propose two modifications of Firth's logistic regression using reparametrization, called reparametrized Firth's logistic regression (rFLR) and ridge-penalized reparametrized Firth's logistic regression (RrFLR). The proposed methods are designed by directly reducing the small-sample bias of the maximum likelihood estimate for the parameter of interest. In addition, we develop a method on how to construct confidence intervals for rFLR and RrFLR using profile penalized likelihood. In the up-and-down biased-coin design, numerical studies confirm the superior performance of the proposed methods in terms of the mean squared error, bias, and coverage accuracy of confidence intervals.

在临床开发过程中,通过揭示剂量-反应关系来找到适当的药物剂量是一个非常关键且具有挑战性的问题。剂量寻找研究的主要关注点是确定麻醉研究中的最小有效剂量(MED)和肿瘤临床试验中的最大耐受剂量(MTD)。为了估算 MED 和 MTD,我们提出了两种使用重拟态对 Firth Logistic 回归进行修改的方法,分别称为重拟态 Firth Logistic 回归(rFLR)和脊惩罚重拟态 Firth Logistic 回归(RrFLR)。所提出的方法是通过直接减少相关参数的最大似然估计的小样本偏差而设计的。此外,我们还开发了一种方法,即如何利用轮廓惩罚似然法构建 rFLR 和 RrFLR 的置信区间。在上下偏置硬币设计中,数值研究证实了所提方法在均方误差、偏差和置信区间覆盖精度方面的优越性能。
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引用次数: 0
Sample Size Estimation Using a Partially Clustered Frailty Model for Biomarker-Strategy Designs With Multiple Treatments. 使用部分聚类虚弱模型估算具有多种治疗方法的生物标记物策略设计的样本量。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-16 DOI: 10.1002/pst.2407
Derek Dinart, Virginie Rondeau, Carine Bellera

Biomarker-guided therapy is a growing area of research in medicine. To optimize the use of biomarkers, several study designs including the biomarker-strategy design (BSD) have been proposed. Unlike traditional designs, the emphasis here is on comparing treatment strategies and not on treatment molecules as such. Patients are assigned to either a biomarker-based strategy (BBS) arm, in which biomarker-positive patients receive an experimental treatment that targets the identified biomarker, or a non-biomarker-based strategy (NBBS) arm, in which patients receive treatment regardless of their biomarker status. We proposed a simulation method based on a partially clustered frailty model (PCFM) as well as an extension of Freidlin formula to estimate the sample size required for BSD with multiple targeted treatments. The sample size was mainly influenced by the heterogeneity of treatment effect, the proportion of biomarker-negative patients, and the randomization ratio. The PCFM is well suited for the data structure and offers an alternative to traditional methodologies.

生物标志物指导疗法是一个不断发展的医学研究领域。为了优化生物标记物的使用,人们提出了包括生物标记物策略设计(BSD)在内的多种研究设计。与传统设计不同的是,这里的重点是比较治疗策略,而不是治疗分子本身。患者被分配到基于生物标记物的策略(BBS)组或非基于生物标记物的策略(NBBS)组,在BBS组中,生物标记物阳性患者接受针对已确定生物标记物的实验性治疗;在NBBS组中,患者无论其生物标记物状态如何都接受治疗。我们提出了一种基于部分聚类虚弱模型(PCFM)的模拟方法以及 Freidlin 公式的扩展,用于估算采用多种靶向治疗的 BSD 所需的样本量。样本量主要受治疗效果异质性、生物标志物阴性患者比例和随机化比例的影响。PCFM 非常适合数据结构,是传统方法的替代方案。
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引用次数: 0
Handling Partially Observed Trial Data After Treatment Withdrawal: Introducing Retrieved Dropout Reference-Base Centred Multiple Imputation. 处理治疗退出后的部分观察试验数据:引入以检索到的辍学参考基数为中心的多重估算。
IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pub Date : 2024-07-16 DOI: 10.1002/pst.2416
Suzie Cro, James H Roger, James R Carpenter

The ICH E9(R1) Addendum (International Council for Harmonization 2019) suggests treatment-policy as one of several strategies for addressing intercurrent events such as treatment withdrawal when defining an estimand. This strategy requires the monitoring of patients and collection of primary outcome data following termination of randomised treatment. However, when patients withdraw from a study early before completion this creates true missing data complicating the analysis. One possible way forward uses multiple imputation to replace the missing data based on a model for outcome on- and off-treatment prior to study withdrawal, often referred to as retrieved dropout multiple imputation. This article introduces a novel approach to parameterising this imputation model so that those parameters which may be difficult to estimate have mildly informative Bayesian priors applied during the imputation stage. A core reference-based model is combined with a retrieved dropout compliance model, using both on- and off-treatment data, to form an extended model for the purposes of imputation. This alleviates the problem of specifying a complex set of analysis rules to accommodate situations where parameters which influence the estimated value are not estimable, or are poorly estimated leading to unrealistically large standard errors in the resulting analysis. We refer to this new approach as retrieved dropout reference-base centred multiple imputation.

ICH E9(R1)增编(国际协调理事会,2019 年)建议,在定义估算指标时,将治疗政策作为解决治疗退出等并发症的几种策略之一。该策略要求对患者进行监测,并在随机治疗终止后收集主要结果数据。但是,如果患者在研究完成前提前退出,就会造成真正的数据缺失,使分析变得复杂。一种可行的方法是使用多重归因法来替换缺失数据,该方法基于研究退出前治疗中和治疗后的结果模型,通常称为检索辍学多重归因法。本文介绍了一种新颖的方法来为这一估算模型设置参数,以便在估算阶段对那些可能难以估计的参数应用轻度信息贝叶斯先验。基于参考文献的核心模型与检索到的辍学顺应性模型相结合,同时使用治疗中和治疗后的数据,形成一个用于估算的扩展模型。这就减轻了指定一套复杂的分析规则的问题,以适应影响估计值的参数无法估计或估计不准确导致分析结果标准误差过大的情况。我们将这种新方法称为以检索辍学参考基数为中心的多重估算。
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引用次数: 0
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Pharmaceutical Statistics
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