Pub Date : 2024-07-01Epub Date: 2024-02-05DOI: 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。
{"title":"A case study: Assessing the efficacy of the revised dosage regimen via prediction model for recurrent event rate using biomarker data.","authors":"Ahrim Youn, Jiarui Chi, Yue Cui, Hui Quan","doi":"10.1002/pst.2362","DOIUrl":"10.1002/pst.2362","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"570-584"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139692642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-02-07DOI: 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。
{"title":"Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation.","authors":"Björn Bornkamp, Silvia Zaoli, Michela Azzarito, Ruvie Martin, Carsten Philipp Müller, Conor Moloney, Giulia Capestro, David Ohlssen, Mark Baillie","doi":"10.1002/pst.2368","DOIUrl":"10.1002/pst.2368","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"495-510"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139703123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-02-08DOI: 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.
{"title":"Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence.","authors":"Awa Diop, Alind Gupta, Sabrina Mueller, Louis Dron, Ofir Harari, Heather Berringer, Vinusha Kalatharan, Jay J H Park, Miceline Mésidor, Denis Talbot","doi":"10.1002/pst.2365","DOIUrl":"10.1002/pst.2365","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"511-529"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139703122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-02-14DOI: 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.
{"title":"On sample size calculation in drug interaction trials.","authors":"Paul Meyvisch, Mitra Ebrahimpoor","doi":"10.1002/pst.2367","DOIUrl":"10.1002/pst.2367","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"530-539"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139735785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-02-05DOI: 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。
{"title":"On the relative conservativeness of Bayesian logistic regression method in oncology dose-finding studies.","authors":"Cheng-Han Yang, Guanghui Cheng, Ruitao Lin","doi":"10.1002/pst.2364","DOIUrl":"10.1002/pst.2364","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"585-594"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139692643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-01-17DOI: 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.
{"title":"Transporting randomized trial results to estimate counterfactual survival functions in target populations.","authors":"Zhiqiang Cao, Youngjoo Cho, Fan Li","doi":"10.1002/pst.2354","DOIUrl":"10.1002/pst.2354","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"442-465"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139484744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-02-23DOI: 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。
{"title":"Shrinkage priors for isotonic probability vectors and binary data modeling, with applications to dose-response modeling.","authors":"Philip S Boonstra, Daniel R Owen, Jian Kang","doi":"10.1002/pst.2372","DOIUrl":"10.1002/pst.2372","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"540-556"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139940445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-01-28DOI: 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。
{"title":"The flaw of averages: Bayes factors as posterior means of the likelihood ratio.","authors":"Charles C Liu, Ron Xiaolong Yu, Murray Aitkin","doi":"10.1002/pst.2355","DOIUrl":"10.1002/pst.2355","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"466-479"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139569571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01Epub Date: 2024-01-31DOI: 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。
{"title":"A generalized Bayesian optimal interval design for dose optimization in immunotherapy.","authors":"Qing Xia, Kentaro Takeda, Yusuke Yamaguchi, Jun Zhang","doi":"10.1002/pst.2369","DOIUrl":"10.1002/pst.2369","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"480-494"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139723543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Investigating Stability in Subgroup Identification for Stratified Medicine.","authors":"G M Hair, T Jemielita, S Mt-Isa, P M Schnell, R Baumgartner","doi":"10.1002/pst.2409","DOIUrl":"https://doi.org/10.1002/pst.2409","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141458676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}