Pub Date : 2024-09-01Epub Date: 2024-03-29DOI: 10.1002/pst.2386
Jonathan M Siegel, Hans-Jochen Weber, Stefan Englert, Feng Liu, Michelle Casey
Time-to-event estimands are central to many oncology clinical trials. The estimands framework (addendum to the ICH E9 guideline) calls for precisely defining the treatment effect of interest to align with the clinical question of interest and requires predefining the handling of intercurrent events (ICEs) that occur after treatment initiation and "affect either the interpretation or the existence of the measurements associated with the clinical question of interest." We discuss a practical problem in clinical trial design and execution, that is, in some clinical contexts it is not feasible to systematically follow patients to an event of interest. Loss to follow-up in the presence of intercurrent events can affect the meaning and interpretation of the study results. We provide recommendations for trial design, stressing the need for close alignment of the clinical question of interest and study design, impact on data collection, and other practical implications. When patients cannot be systematically followed, compromise may be necessary to select the best available estimand that can be feasibly estimated under the circumstances. We discuss the use of sensitivity and supplementary analyses to examine assumptions of interest.
{"title":"Time-to-event estimands and loss to follow-up in oncology in light of the estimands guidance.","authors":"Jonathan M Siegel, Hans-Jochen Weber, Stefan Englert, Feng Liu, Michelle Casey","doi":"10.1002/pst.2386","DOIUrl":"10.1002/pst.2386","url":null,"abstract":"<p><p>Time-to-event estimands are central to many oncology clinical trials. The estimands framework (addendum to the ICH E9 guideline) calls for precisely defining the treatment effect of interest to align with the clinical question of interest and requires predefining the handling of intercurrent events (ICEs) that occur after treatment initiation and \"affect either the interpretation or the existence of the measurements associated with the clinical question of interest.\" We discuss a practical problem in clinical trial design and execution, that is, in some clinical contexts it is not feasible to systematically follow patients to an event of interest. Loss to follow-up in the presence of intercurrent events can affect the meaning and interpretation of the study results. We provide recommendations for trial design, stressing the need for close alignment of the clinical question of interest and study design, impact on data collection, and other practical implications. When patients cannot be systematically followed, compromise may be necessary to select the best available estimand that can be feasibly estimated under the circumstances. We discuss the use of sensitivity and supplementary analyses to examine assumptions of interest.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"709-727"},"PeriodicalIF":16.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140326928","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 : 2024-09-01Epub Date: 2024-03-10DOI: 10.1002/pst.2378
Shiwei Cao, Sin-Ho Jung
A multi-stage design for a randomized trial is to allow early termination of the study when the experimental arm is found to have low or high efficacy compared to the control during the study. In such a trial, an early stopping rule results in bias in the maximum likelihood estimator of the treatment effect. We consider multi-stage randomized trials on a dichotomous outcome, such as treatment response, and investigate the estimation of the odds ratio. Typically, randomized phase II cancer clinical trials have two-stage designs with small sample sizes, which makes the estimation of odds ratio more challenging. In this paper, we evaluate several existing estimation methods of odds ratio and propose bias-corrected estimators for randomized multi-stage trials, including randomized phase II cancer clinical trials. Through numerical studies, the proposed estimators are shown to have a smaller bias and a smaller mean squared error overall.
随机试验的多阶段设计是为了在研究过程中发现试验组与对照组相比疗效较低或较高时,允许提前终止研究。在这种试验中,提前终止规则会导致治疗效果最大似然估计值出现偏差。我们考虑了关于二分结果(如治疗反应)的多阶段随机试验,并研究了几率比的估计。通常情况下,II 期随机癌症临床试验采用两阶段设计,样本量较小,这使得几率比的估计更具挑战性。本文评估了几种现有的几率比估计方法,并提出了适用于随机多阶段试验(包括随机 II 期癌症临床试验)的偏差校正估计器。通过数值研究表明,所提出的估计方法总体上具有较小的偏差和较小的均方误差。
{"title":"Estimation of the odds ratio from multi-stage randomized trials.","authors":"Shiwei Cao, Sin-Ho Jung","doi":"10.1002/pst.2378","DOIUrl":"10.1002/pst.2378","url":null,"abstract":"<p><p>A multi-stage design for a randomized trial is to allow early termination of the study when the experimental arm is found to have low or high efficacy compared to the control during the study. In such a trial, an early stopping rule results in bias in the maximum likelihood estimator of the treatment effect. We consider multi-stage randomized trials on a dichotomous outcome, such as treatment response, and investigate the estimation of the odds ratio. Typically, randomized phase II cancer clinical trials have two-stage designs with small sample sizes, which makes the estimation of odds ratio more challenging. In this paper, we evaluate several existing estimation methods of odds ratio and propose bias-corrected estimators for randomized multi-stage trials, including randomized phase II cancer clinical trials. Through numerical studies, the proposed estimators are shown to have a smaller bias and a smaller mean squared error overall.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"662-677"},"PeriodicalIF":16.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140094414","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 : 2024-09-01Epub Date: 2024-03-29DOI: 10.1002/pst.2387
Anders Granholm, Theis Lange, Michael O Harhay, Anders Perner, Morten Hylander Møller, Benjamin Skov Kaas-Hansen
It is unclear how sceptical priors impact adaptive trials. We assessed the influence of priors expressing a spectrum of scepticism on the performance of several Bayesian, multi-stage, adaptive clinical trial designs using binary outcomes under different clinical scenarios. Simulations were conducted using fixed stopping rules and stopping rules calibrated to keep type 1 error rates at approximately 5%. We assessed total sample sizes, event rates, event counts, probabilities of conclusiveness and selecting the best arm, root mean squared errors (RMSEs) of the estimated treatment effect in the selected arms, and ideal design percentages (IDPs; which combines arm selection probabilities, power, and consequences of selecting inferior arms), with RMSEs and IDPs estimated in conclusive trials only and after selecting the control arm in inconclusive trials. Using fixed stopping rules, increasingly sceptical priors led to larger sample sizes, more events, higher IDPs in simulations ending in superiority, and lower RMSEs, lower probabilities of conclusiveness/selecting the best arm, and lower IDPs when selecting controls in inconclusive simulations. With calibrated stopping rules, the effects of increased scepticism on sample sizes and event counts were attenuated, and increased scepticism increased the probabilities of conclusiveness/selecting the best arm and IDPs when selecting controls in inconclusive simulations without substantially increasing sample sizes. Results from trial designs with gentle adaptation and non-informative priors resembled those from designs with more aggressive adaptation using weakly-to-moderately sceptical priors. In conclusion, the use of somewhat sceptical priors in adaptive trial designs with binary outcomes seems reasonable when considering multiple performance metrics simultaneously.
{"title":"Effects of sceptical priors on the performance of adaptive clinical trials with binary outcomes.","authors":"Anders Granholm, Theis Lange, Michael O Harhay, Anders Perner, Morten Hylander Møller, Benjamin Skov Kaas-Hansen","doi":"10.1002/pst.2387","DOIUrl":"10.1002/pst.2387","url":null,"abstract":"<p><p>It is unclear how sceptical priors impact adaptive trials. We assessed the influence of priors expressing a spectrum of scepticism on the performance of several Bayesian, multi-stage, adaptive clinical trial designs using binary outcomes under different clinical scenarios. Simulations were conducted using fixed stopping rules and stopping rules calibrated to keep type 1 error rates at approximately 5%. We assessed total sample sizes, event rates, event counts, probabilities of conclusiveness and selecting the best arm, root mean squared errors (RMSEs) of the estimated treatment effect in the selected arms, and ideal design percentages (IDPs; which combines arm selection probabilities, power, and consequences of selecting inferior arms), with RMSEs and IDPs estimated in conclusive trials only and after selecting the control arm in inconclusive trials. Using fixed stopping rules, increasingly sceptical priors led to larger sample sizes, more events, higher IDPs in simulations ending in superiority, and lower RMSEs, lower probabilities of conclusiveness/selecting the best arm, and lower IDPs when selecting controls in inconclusive simulations. With calibrated stopping rules, the effects of increased scepticism on sample sizes and event counts were attenuated, and increased scepticism increased the probabilities of conclusiveness/selecting the best arm and IDPs when selecting controls in inconclusive simulations without substantially increasing sample sizes. Results from trial designs with gentle adaptation and non-informative priors resembled those from designs with more aggressive adaptation using weakly-to-moderately sceptical priors. In conclusion, the use of somewhat sceptical priors in adaptive trial designs with binary outcomes seems reasonable when considering multiple performance metrics simultaneously.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"728-741"},"PeriodicalIF":1.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11438950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140326927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01Epub Date: 2024-03-04DOI: 10.1002/pst.2376
Carl-Fredrik Burman, Erik Hermansson, David Bock, Stefan Franzén, David Svensson
Recent years have seen an increasing interest in incorporating external control data for designing and evaluating randomized clinical trials (RCT). This may decrease costs and shorten inclusion times by reducing sample sizes. For small populations, with limited recruitment, this can be especially important. Bayesian dynamic borrowing (BDB) has been a popular choice as it claims to protect against potential prior data conflict. Digital twins (DT) has recently been proposed as another method to utilize historical data. DT, also known as PROCOVA™, is based on constructing a prognostic score from historical control data, typically using machine learning. This score is included in a pre-specified ANCOVA as the primary analysis of the RCT. The promise of this idea is power increase while guaranteeing strong type 1 error control. In this paper, we apply analytic derivations and simulations to analyze and discuss examples of these two approaches. We conclude that BDB and DT, although similar in scope, have fundamental differences which need be considered in the specific application. The inflation of the type 1 error is a serious issue for BDB, while more evidence is needed of a tangible value of DT for real RCTs.
{"title":"Digital twins and Bayesian dynamic borrowing: Two recent approaches for incorporating historical control data.","authors":"Carl-Fredrik Burman, Erik Hermansson, David Bock, Stefan Franzén, David Svensson","doi":"10.1002/pst.2376","DOIUrl":"10.1002/pst.2376","url":null,"abstract":"<p><p>Recent years have seen an increasing interest in incorporating external control data for designing and evaluating randomized clinical trials (RCT). This may decrease costs and shorten inclusion times by reducing sample sizes. For small populations, with limited recruitment, this can be especially important. Bayesian dynamic borrowing (BDB) has been a popular choice as it claims to protect against potential prior data conflict. Digital twins (DT) has recently been proposed as another method to utilize historical data. DT, also known as PROCOVA™, is based on constructing a prognostic score from historical control data, typically using machine learning. This score is included in a pre-specified ANCOVA as the primary analysis of the RCT. The promise of this idea is power increase while guaranteeing strong type 1 error control. In this paper, we apply analytic derivations and simulations to analyze and discuss examples of these two approaches. We conclude that BDB and DT, although similar in scope, have fundamental differences which need be considered in the specific application. The inflation of the type 1 error is a serious issue for BDB, while more evidence is needed of a tangible value of DT for real RCTs.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"611-629"},"PeriodicalIF":16.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140028706","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 : 2024-09-01Epub Date: 2024-03-20DOI: 10.1002/pst.2382
Julie Funch Furberg, Per Kragh Andersen, Thomas Scheike, Henrik Ravn
In randomised controlled trials, the outcome of interest could be recurrent events, such as hospitalisations for heart failure. If mortality rates are non-negligible, both recurrent events and competing terminal events need to be addressed when formulating the estimand and statistical analysis is no longer trivial. In order to design future trials with primary recurrent event endpoints with competing risks, it is necessary to be able to perform power calculations to determine sample sizes. This paper introduces a simulation-based approach for power estimation based on a proportional means model for recurrent events and a proportional hazards model for terminal events. The simulation procedure is presented along with a discussion of what the user needs to specify to use the approach. The method is flexible and based on marginal quantities which are easy to specify. However, the method introduces a lack of a certain type of dependence. This is explored in a sensitivity analysis which suggests that the power is robust in spite of that. Data from a randomised controlled trial, LEADER, is used as the basis for generating data for a future trial. Finally, potential power gains of recurrent event methods as opposed to first event methods are discussed.
{"title":"Simulation-based sample size calculations of marginal proportional means models for recurrent events with competing risks.","authors":"Julie Funch Furberg, Per Kragh Andersen, Thomas Scheike, Henrik Ravn","doi":"10.1002/pst.2382","DOIUrl":"10.1002/pst.2382","url":null,"abstract":"<p><p>In randomised controlled trials, the outcome of interest could be recurrent events, such as hospitalisations for heart failure. If mortality rates are non-negligible, both recurrent events and competing terminal events need to be addressed when formulating the estimand and statistical analysis is no longer trivial. In order to design future trials with primary recurrent event endpoints with competing risks, it is necessary to be able to perform power calculations to determine sample sizes. This paper introduces a simulation-based approach for power estimation based on a proportional means model for recurrent events and a proportional hazards model for terminal events. The simulation procedure is presented along with a discussion of what the user needs to specify to use the approach. The method is flexible and based on marginal quantities which are easy to specify. However, the method introduces a lack of a certain type of dependence. This is explored in a sensitivity analysis which suggests that the power is robust in spite of that. Data from a randomised controlled trial, LEADER, is used as the basis for generating data for a future trial. Finally, potential power gains of recurrent event methods as opposed to first event methods are discussed.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"687-708"},"PeriodicalIF":16.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140175941","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}
The sample size of a clinical trial has to be large enough to ensure sufficient power for achieving the aim the study. On the other side, for ethical and economical reasons it should not be larger than necessary. The sample size allocation is one of the parameters that influences the required total sample size. For two-arm superiority and non-inferiority trials with binary endpoints, we performed extensive computations over a wide range of scenarios to determine the optimal allocation ratio that minimizes the total sample size if all other parameters are fixed. The results demonstrate, that for both superiority and non-inferiority trials the optimal allocation may deviate considerably from the case of equal sample size in both groups. However, the saving in sample size when allocating the total sample size optimally as compared to balanced allocation is typically small.
{"title":"Optimal sample size allocation for two-arm superiority and non-inferiority trials with binary endpoints.","authors":"Marietta Kirchner, Stefanie Schüpke, Meinhard Kieser","doi":"10.1002/pst.2375","DOIUrl":"10.1002/pst.2375","url":null,"abstract":"<p><p>The sample size of a clinical trial has to be large enough to ensure sufficient power for achieving the aim the study. On the other side, for ethical and economical reasons it should not be larger than necessary. The sample size allocation is one of the parameters that influences the required total sample size. For two-arm superiority and non-inferiority trials with binary endpoints, we performed extensive computations over a wide range of scenarios to determine the optimal allocation ratio that minimizes the total sample size if all other parameters are fixed. The results demonstrate, that for both superiority and non-inferiority trials the optimal allocation may deviate considerably from the case of equal sample size in both groups. However, the saving in sample size when allocating the total sample size optimally as compared to balanced allocation is typically small.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"678-686"},"PeriodicalIF":16.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140111096","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}
Sami Leon, Elena Rantou, Jessica Kim, Sungwoo Choi, Nam Hee Choi
For topical, dermatological drug products, an in vitro option to determine bioequivalence (BE) between test and reference products is recommended. In particular, in vitro permeation test (IVPT) data analysis uses a reference-scaled approach for two primary endpoints, cumulative penetration amount (AMT) and maximum flux (Jmax), which takes the within donor variability into consideration. In 2022, the Food and Drug Administration (FDA) published a draft IVPT guidance that includes statistical analysis methods for both balanced and unbalanced cases of IVPT study data. This work presents a comprehensive evaluation of various methodologies used to estimate critical parameters essential in assessing BE. Specifically, we investigate the performance of the FDA draft IVPT guidance approach alongside alternative empirical and model-based methods utilizing mixed-effects models. Our analyses include both simulated scenarios and real-world studies. In simulated scenarios, empirical formulas consistently demonstrate robustness in approximating the true model, particularly in effectively addressing treatment-donor interactions. Conversely, the effectiveness of model-based approaches heavily relies on precise model selection, which significantly influences their results. The research emphasizes the importance of accurate model selection in model-based BE assessment methodologies. It sheds light on the advantages of empirical formulas, highlighting their reliability compared to model-based approaches and offers valuable implications for BE assessments. Our findings underscore the significance of robust methodologies and provide essential insights to advance their understanding and application in the assessment of BE, employed in IVPT data analysis.
对于外用皮肤病药物产品,建议采用体外方法来确定试验产品和参照产品之间的生物等效性(BE)。特别是,体外渗透试验(IVPT)数据分析对两个主要终点--累积渗透量(AMT)和最大通量(Jmax)--采用参考标度法,其中考虑了供体内部的变异性。2022 年,美国食品和药物管理局(FDA)发布了 IVPT 指南草案,其中包括 IVPT 研究数据平衡和非平衡情况的统计分析方法。这项工作全面评估了用于估算评估 BE 所必需的关键参数的各种方法。具体来说,我们研究了 FDA IVPT 指南草案方法的性能,以及利用混合效应模型的其他基于经验和模型的方法。我们的分析包括模拟情景和真实世界研究。在模拟场景中,经验公式在逼近真实模型方面始终表现出稳健性,尤其是在有效处理治疗-供体相互作用方面。相反,基于模型的方法的有效性在很大程度上依赖于精确的模型选择,这对其结果有很大影响。这项研究强调了在基于模型的生物多样性评估方法中准确选择模型的重要性。研究揭示了经验公式的优势,强调了与基于模型的方法相比,经验公式的可靠性,并为生物多样性评估提供了有价值的启示。我们的研究结果强调了稳健方法的重要性,并为在 IVPT 数据分析中使用的 BE 评估方法的理解和应用提供了重要启示。
{"title":"Comparative Analyses of Bioequivalence Assessment Methods for In Vitro Permeation Test Data.","authors":"Sami Leon, Elena Rantou, Jessica Kim, Sungwoo Choi, Nam Hee Choi","doi":"10.1002/pst.2434","DOIUrl":"https://doi.org/10.1002/pst.2434","url":null,"abstract":"<p><p>For topical, dermatological drug products, an in vitro option to determine bioequivalence (BE) between test and reference products is recommended. In particular, in vitro permeation test (IVPT) data analysis uses a reference-scaled approach for two primary endpoints, cumulative penetration amount (AMT) and maximum flux (J<sub>max</sub>), which takes the within donor variability into consideration. In 2022, the Food and Drug Administration (FDA) published a draft IVPT guidance that includes statistical analysis methods for both balanced and unbalanced cases of IVPT study data. This work presents a comprehensive evaluation of various methodologies used to estimate critical parameters essential in assessing BE. Specifically, we investigate the performance of the FDA draft IVPT guidance approach alongside alternative empirical and model-based methods utilizing mixed-effects models. Our analyses include both simulated scenarios and real-world studies. In simulated scenarios, empirical formulas consistently demonstrate robustness in approximating the true model, particularly in effectively addressing treatment-donor interactions. Conversely, the effectiveness of model-based approaches heavily relies on precise model selection, which significantly influences their results. The research emphasizes the importance of accurate model selection in model-based BE assessment methodologies. It sheds light on the advantages of empirical formulas, highlighting their reliability compared to model-based approaches and offers valuable implications for BE assessments. Our findings underscore the significance of robust methodologies and provide essential insights to advance their understanding and application in the assessment of BE, employed in IVPT data analysis.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047000","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}
Ludwig A Hothorn, Christian Ritz, Frank Schaarschmidt, Signe M Jensen, Robin Ristl
This tutorial describes single-step low-dimensional simultaneous inference with a focus on the availability of adjusted p values and compatible confidence intervals for more than just the usual mean value comparisons. The basic idea is, first, to use the influence of correlation on the quantile of the multivariate t-distribution: the higher the less conservative. In addition, second, the estimability of the correlation matrix using the multiple marginal models approach (mmm) using multiple models in the class of linear up to generalized linear mixed models. The underlying maxT-test using mmm is discussed by means of several real data scenarios using selected R packages. Surprisingly, different features are highlighted, among them: (i) analyzing different-scaled, correlated, multiple endpoints, (ii) analyzing multiple correlated binary endpoints, (iii) modeling dose as qualitative factor and/or quantitative covariate, (iv) joint consideration of several tuning parameters within the poly-k trend test, (v) joint testing of dose and time, (vi) considering several effect sizes, (vii) joint testing of subgroups and overall population in multiarm randomized clinical trials with correlated primary endpoints, (viii) multiple linear mixed effect models, (ix) generalized estimating equations, and (x) nonlinear regression models.
本教程介绍了单步低维同步推理,重点是调整后 p 值和兼容置信区间的可用性,而不仅仅是通常的均值比较。其基本思想是:首先,利用相关性对多元 t 分布的量值的影响:越高越不保守。此外,第二,使用多重边际模型方法(mmm),使用线性到广义线性混合模型类中的多重模型来估算相关矩阵的可估算性。使用选定的 R 软件包,通过几个真实数据场景讨论了使用 mmm 的基本 maxT 检验。令人惊讶的是,其中突出了不同的特点:(i) 分析不同尺度、相关的多个终点,(ii) 分析多个相关的二进制终点,(iii) 将剂量作为定性因子和/或定量协变量建模,(iv) 在 poly-k 趋势检验中联合考虑多个调整参数,(v) 联合检验剂量和时间、(viii) 多重线性混合效应模型;(ix) 广义估计方程;以及 (x) 非线性回归模型。
{"title":"Simultaneous Inference Using Multiple Marginal Models.","authors":"Ludwig A Hothorn, Christian Ritz, Frank Schaarschmidt, Signe M Jensen, Robin Ristl","doi":"10.1002/pst.2428","DOIUrl":"https://doi.org/10.1002/pst.2428","url":null,"abstract":"<p><p>This tutorial describes single-step low-dimensional simultaneous inference with a focus on the availability of adjusted p values and compatible confidence intervals for more than just the usual mean value comparisons. The basic idea is, first, to use the influence of correlation on the quantile of the multivariate t-distribution: the higher the less conservative. In addition, second, the estimability of the correlation matrix using the multiple marginal models approach (mmm) using multiple models in the class of linear up to generalized linear mixed models. The underlying maxT-test using mmm is discussed by means of several real data scenarios using selected R packages. Surprisingly, different features are highlighted, among them: (i) analyzing different-scaled, correlated, multiple endpoints, (ii) analyzing multiple correlated binary endpoints, (iii) modeling dose as qualitative factor and/or quantitative covariate, (iv) joint consideration of several tuning parameters within the poly-k trend test, (v) joint testing of dose and time, (vi) considering several effect sizes, (vii) joint testing of subgroups and overall population in multiarm randomized clinical trials with correlated primary endpoints, (viii) multiple linear mixed effect models, (ix) generalized estimating equations, and (x) nonlinear regression models.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142009206","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}
Stochastic curtailment tests for Phase II two-arm trials with time-to-event end points are traditionally performed using the log-rank test. Recent advances in designing time-to-event trials have utilized the Weibull distribution with a known shape parameter estimated from historical studies. As sample size calculations depend on the value of this shape parameter, these methods either cannot be used or likely underperform/overperform when the natural variation around the point estimate is ignored. We demonstrate that when the magnitude of the Weibull shape parameters changes, unblinded interim information on the shape of the survival curves can be useful to enrich the final analysis for reestimation of the sample size. For such scenarios, we propose two Bayesian solutions to estimate the natural variations of the Weibull shape parameter. We implement these approaches under the framework of the newly proposed relative time method that allows nonproportional hazards and nonproportional time. We also demonstrate the sample size reestimation for the relative time method using three different approaches (internal pilot study approach, conditional power, and predictive power approach) at the interim stage of the trial. We demonstrate our methods using a hypothetical example and provide insights regarding the practical constraints for the proposed methods.
{"title":"Sample Size Reestimation in Stochastic Curtailment Tests With Time-to-Events Outcome in the Case of Nonproportional Hazards Utilizing Two Weibull Distributions With Unknown Shape Parameters.","authors":"Palash Sharma, Milind A Phadnis","doi":"10.1002/pst.2429","DOIUrl":"https://doi.org/10.1002/pst.2429","url":null,"abstract":"<p><p>Stochastic curtailment tests for Phase II two-arm trials with time-to-event end points are traditionally performed using the log-rank test. Recent advances in designing time-to-event trials have utilized the Weibull distribution with a known shape parameter estimated from historical studies. As sample size calculations depend on the value of this shape parameter, these methods either cannot be used or likely underperform/overperform when the natural variation around the point estimate is ignored. We demonstrate that when the magnitude of the Weibull shape parameters changes, unblinded interim information on the shape of the survival curves can be useful to enrich the final analysis for reestimation of the sample size. For such scenarios, we propose two Bayesian solutions to estimate the natural variations of the Weibull shape parameter. We implement these approaches under the framework of the newly proposed relative time method that allows nonproportional hazards and nonproportional time. We also demonstrate the sample size reestimation for the relative time method using three different approaches (internal pilot study approach, conditional power, and predictive power approach) at the interim stage of the trial. We demonstrate our methods using a hypothetical example and provide insights regarding the practical constraints for the proposed methods.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142000525","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}
Tobias Mütze, James Bell, Stefan Englert, Philip Hougaard, Dan Jackson, Vivian Lanius, Henrik Ravn
The ICH E9(R1) guideline outlines the estimand framework, which aligns planning, design, conduct, analysis, and interpretation of a clinical trial. The benefits and value of using this framework in clinical trials have been outlined in the literature, and guidance has been provided on how to choose the estimand and define the estimand attributes. Although progress has been made in the implementation of estimands in clinical trials, to the best of our knowledge, there is no published discussion on the basic principles that estimands in clinical trials should fulfill to be well defined and consistent with the ideas presented in the ICH E9(R1) guideline. Therefore, in this Viewpoint article, we propose four key principles for defining an estimand. These principles form a basis for well-defined treatment effects that reflect the estimand thinking process. We hope that this Viewpoint will complement ICH E9(R1) and stimulate a discussion on which fundamental properties an estimand in a clinical trial should have and that such discussions will eventually lead to an improved clarity and precision for defining estimands in clinical trials.
ICH E9(R1)指南概述了临床试验的规划、设计、实施、分析和解释的估算指标框架。文献中概述了在临床试验中使用该框架的好处和价值,并就如何选择估计指标和定义估计指标属性提供了指导。尽管在临床试验中实施估计指标方面取得了进展,但据我们所知,目前还没有关于临床试验中的估计指标应符合哪些基本原则的公开讨论,这些原则应定义明确,并与 ICH E9(R1) 指南中提出的观点保持一致。因此,在这篇观点文章中,我们提出了定义估算指标的四项关键原则。这些原则构成了定义明确的治疗效果的基础,反映了估计值的思维过程。我们希望本观点能够补充 ICH E9(R1),并激发关于临床试验中的估计指标应具备哪些基本属性的讨论,并希望这些讨论最终能够提高临床试验中定义估计指标的清晰度和精确度。
{"title":"Principles for Defining Estimands in Clinical Trials-A Proposal.","authors":"Tobias Mütze, James Bell, Stefan Englert, Philip Hougaard, Dan Jackson, Vivian Lanius, Henrik Ravn","doi":"10.1002/pst.2432","DOIUrl":"https://doi.org/10.1002/pst.2432","url":null,"abstract":"<p><p>The ICH E9(R1) guideline outlines the estimand framework, which aligns planning, design, conduct, analysis, and interpretation of a clinical trial. The benefits and value of using this framework in clinical trials have been outlined in the literature, and guidance has been provided on how to choose the estimand and define the estimand attributes. Although progress has been made in the implementation of estimands in clinical trials, to the best of our knowledge, there is no published discussion on the basic principles that estimands in clinical trials should fulfill to be well defined and consistent with the ideas presented in the ICH E9(R1) guideline. Therefore, in this Viewpoint article, we propose four key principles for defining an estimand. These principles form a basis for well-defined treatment effects that reflect the estimand thinking process. We hope that this Viewpoint will complement ICH E9(R1) and stimulate a discussion on which fundamental properties an estimand in a clinical trial should have and that such discussions will eventually lead to an improved clarity and precision for defining estimands in clinical trials.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976330","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}