Pharmaceutical Statistics最新文献_第3页

Success and Futility Criteria for Accelerated Approval of Oncology Drugs. 肿瘤药物加速审批的成功和无效标准。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 DOI: 10.1002/pst.70004

Dong Xi, Jiangtao Gou

{"title":"Success and Futility Criteria for Accelerated Approval of Oncology Drugs.","authors":"Dong Xi, Jiangtao Gou","doi":"10.1002/pst.70004","DOIUrl":"10.1002/pst.70004","url":null,"abstract":"Project FrontRunner encourages development of cancer drugs for advanced or metastatic disease in an earlier clinical setting by promoting regulatory approaches such as the accelerated approval pathway. The FDA draft guideline proposes a one-trial approach to combine accelerated approval and regular approval in a single trial to maintain efficiency. This article describes our idea of controlling Type I error for accelerated and regular approvals in the one-trial approach. We introduce success and futility boundaries on p-values for accelerated approval to create three outcomes: success, RA, and futility. If success, accelerated approval can be claimed for; for RA, only regular approval (RA) is considered; if futility, we stop the trial early for futility. For both success and RA, the endpoint for regular approval can be tested with no penalty on its significance level. The proposed approach is robust to all possible values of correlation between test statistics of the endpoints for accelerated and regular approvals. This framework is flexible to allow clinical trial teams to tailor success and futility boundaries to meet clinical and regulatory needs, while maintaining the overall Type I error control in the strong sense.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 2","pages":"e70004"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143567868","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}

引用次数: 0

Pre-Posterior Distributions in Drug Development and Their Properties. 药物开发中的前后分布及其特性。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-11-25 DOI: 10.1002/pst.2450

Andrew P Grieve

引用次数: 0

Using Subject Level Covariate Information in Bayesian Mixture Models for Basket Trials. 篮子试验贝叶斯混合模型中受试者水平协变量信息的应用。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 DOI: 10.1002/pst.70006

Sneha Govande, Elizabeth H Slate

引用次数: 0

Treatment Effect Measures Under Nonproportional Hazards. 非比例危害下的治疗效果测量。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-10-27 DOI: 10.1002/pst.2449

Dan Jackson, Michael Sweeting, Rose Baker

引用次数: 0

A Federated Data Analysis Approach for the Evaluation of Surrogate Endpoints. 代理端点评估的联邦数据分析方法。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 DOI: 10.1002/pst.70003

Dries De Witte, Ariel Alonso Abad, Diane Stephenson, Yashmin Karten, Antoine Leuzy, Gregory Klein, Geert Molenberghs

{"title":"A Federated Data Analysis Approach for the Evaluation of Surrogate Endpoints.","authors":"Dries De Witte, Ariel Alonso Abad, Diane Stephenson, Yashmin Karten, Antoine Leuzy, Gregory Klein, Geert Molenberghs","doi":"10.1002/pst.70003","DOIUrl":"10.1002/pst.70003","url":null,"abstract":"In clinical trials, surrogate endpoints, that are more cost-effective, occur earlier, or are more frequently measured, are sometimes used to replace costly, late, or rare true endpoints. Regulatory authorities typically require thorough evaluation and validation to accept these surrogate endpoints as reliable substitutes. To this end, the meta-analytic framework is considered a very viable approach to validate surrogates at both trial and individual levels. However, this framework requires data from multiple trials or centers, posing challenges when data sharing is not feasible. In this article, we propose a federated data analysis approach that allows organizations to maintain control over their datasets while still enabling surrogate validation through meta-analytic techniques. In this approach, there is no longer a need for raw data sharing. Instead, independent analyses are conducted at each organization. Thereafter, the results of these independent analyses are aggregated at a central analysis hub and the metrics for surrogate evaluation are extracted. We apply this approach to simulated and real clinical data, demonstrating how this federated approach can overcome data-sharing constraints and validate surrogate endpoints in decentralized settings.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 2","pages":"e70003"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143503171","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}

引用次数: 0

Real Effect or Bias? Good Practices for Evaluating the Robustness of Evidence From Comparative Observational Studies Through Quantitative Sensitivity Analysis for Unmeasured Confounding. 真实效果还是偏见？通过对未测量混杂的定量敏感性分析评估比较观察性研究证据稳健性的良好实践。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-12-04 DOI: 10.1002/pst.2457

Douglas Faries, Chenyin Gao, Xiang Zhang, Chad Hazlett, James Stamey, Shu Yang, Peng Ding, Mingyang Shan, Kristin Sheffield, Nancy Dreyer

{"title":"Real Effect or Bias? Good Practices for Evaluating the Robustness of Evidence From Comparative Observational Studies Through Quantitative Sensitivity Analysis for Unmeasured Confounding.","authors":"Douglas Faries, Chenyin Gao, Xiang Zhang, Chad Hazlett, James Stamey, Shu Yang, Peng Ding, Mingyang Shan, Kristin Sheffield, Nancy Dreyer","doi":"10.1002/pst.2457","DOIUrl":"10.1002/pst.2457","url":null,"abstract":"The assumption of \"no unmeasured confounders\" is a critical but unverifiable assumption required for causal inference yet quantitative sensitivity analyses to assess robustness of real-world evidence remains under-utilized. The lack of use is likely in part due to complexity of implementation and often specific and restrictive data requirements for application of each method. With the advent of methods that are broadly applicable in that they do not require identification of a specific unmeasured confounder-along with publicly available code for implementation-roadblocks toward broader use of sensitivity analyses are decreasing. To spur greater application, here we offer a good practice guidance to address the potential for unmeasured confounding at both the design and analysis stages, including framing questions and an analytic toolbox for researchers. The questions at the design stage guide the researcher through steps evaluating the potential robustness of the design while encouraging gathering of additional data to reduce uncertainty due to potential confounding. At the analysis stage, the questions guide quantifying the robustness of the observed result and providing researchers with a clearer indication of the strength of their conclusions. We demonstrate the application of this guidance using simulated data based on an observational fibromyalgia study, applying multiple methods from our analytic toolbox for illustration purposes.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"e2457"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142771284","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}

引用次数: 0

Estimation Methods for Estimands Using the Treatment Policy Strategy; a Simulation Study Based on the PIONEER 1 Trial. 基于处理策略策略的估算方法基于PIONEER 1试验的仿真研究。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 DOI: 10.1002/pst.2472

James Bell, Thomas Drury, Tobias Mütze, Christian Bressen Pipper, Lorenzo Guizzaro, Marian Mitroiu, Khadija Rerhou Rantell, Marcel Wolbers, David Wright

{"title":"Estimation Methods for Estimands Using the Treatment Policy Strategy; a Simulation Study Based on the PIONEER 1 Trial.","authors":"James Bell, Thomas Drury, Tobias Mütze, Christian Bressen Pipper, Lorenzo Guizzaro, Marian Mitroiu, Khadija Rerhou Rantell, Marcel Wolbers, David Wright","doi":"10.1002/pst.2472","DOIUrl":"10.1002/pst.2472","url":null,"abstract":"Estimands using the treatment policy strategy for addressing intercurrent events are common in Phase III clinical trials. One estimation approach for this strategy is retrieved dropout whereby observed data following an intercurrent event are used to multiply impute missing data. However, such methods have had issues with variance inflation and model fitting due to data sparsity. This paper introduces likelihood-based versions of these approaches, investigating and comparing their statistical properties to the existing retrieved dropout approaches, simpler analysis models and reference-based multiple imputation. We use a simulation based upon the data from the PIONEER 1 Phase III clinical trial in Type II diabetics to present complex and relevant estimation challenges. The likelihood-based methods display similar statistical properties to their multiple imputation equivalents, but all retrieved dropout approaches suffer from high variance. Retrieved dropout approaches appear less biased than reference-based approaches, resulting in a bias-variance trade-off, but we conclude that the large degree of variance inflation is often more problematic than the bias. Therefore, only the simpler retrieved dropout models appear appropriate as a primary analysis in a clinical trial, and only where it is believed most data following intercurrent events will be observed. The jump-to-reference approach may represent a more promising estimation approach for symptomatic treatments due to its relatively high power and ability to fit in the presence of much missing data, despite its strong assumptions and tendency toward conservative bias. More research is needed to further develop how to estimate the treatment effect for a treatment policy strategy.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 2","pages":"e2472"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143567864","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}

引用次数: 0

Average Hazard as Harmonic Mean. 作为调和平均值的平均危险。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 DOI: 10.1002/pst.70009

Yasutaka Chiba

引用次数: 0

Trial Probability of Success for Testing 3-Way PK/PD Similarity With Multiple Endpoints. 用多个端点测试3-Way PK/PD相似性的试验成功概率。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 DOI: 10.1002/pst.2473

Rachid El Galta, Susanne Schmitt, Ramin Arani, Arne Ring

{"title":"Trial Probability of Success for Testing 3-Way PK/PD Similarity With Multiple Endpoints.","authors":"Rachid El Galta, Susanne Schmitt, Ramin Arani, Arne Ring","doi":"10.1002/pst.2473","DOIUrl":"10.1002/pst.2473","url":null,"abstract":"Pharmacokinetics and pharmacodynamics (PK/PD) similarity trials typically involve multiple coprimary endpoints and a 3-way treatment comparison. The purpose of these trials is to demonstrate the similarity between a biosimilar candidate and two versions of the originator drug. The sample size for these trials is often based on point estimates of the expected treatment difference and/or variability, derived from historical reference data, without considering the uncertainty associated with these estimates. This uncertainty, especially when there are multiple comparisons, can lead to an unreliable estimate of study power. In this paper, we address the power and application of the assurance method in PK/PD similarity studies to account for the uncertainty surrounding treatment differences and/or variability in multiple coprimary endpoints when considering sample size. We introduce an assurance method that can handle multiple comparisons and propose a strategy to elicit joint prior distributions of parameters based on the availability of historical data. These methods are implemented in an R shiny app using the Monte Carlo method. Additionally, we provide a real data example to illustrate the practical application of these methods. Our findings demonstrate that the proposed methods significantly enhance our understanding of study power. Therefore, we recommend incorporating assurance methods as a complement to conditional power in sample size considerations.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 2","pages":"e2473"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143503189","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}

引用次数: 0

BF-BOIN-ET: A Backfill Bayesian Optimal Interval Design Using Efficacy and Toxicity Outcomes for Dose Optimization. BF-BOIN-ET：利用效能和毒性结果进行剂量优化的回填贝叶斯最优间隔设计。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 DOI: 10.1002/pst.2470

Kentaro Takeda, Jing Zhu, Akihiro Hirakawa

{"title":"BF-BOIN-ET: A Backfill Bayesian Optimal Interval Design Using Efficacy and Toxicity Outcomes for Dose Optimization.","authors":"Kentaro Takeda, Jing Zhu, Akihiro Hirakawa","doi":"10.1002/pst.2470","DOIUrl":"10.1002/pst.2470","url":null,"abstract":"The primary purpose of a dose-finding trial for novel anticancer agents is to identify an optimal dose (OD), defined as the tolerable dose that has adequate efficacy in unpredictable dose-toxicity and dose-efficacy relationships. The FDA project Optimus reforms the paradigm of dose optimization and recommends that dose-finding trials compare multiple doses to generate these additional data at promising dose levels. The backfill is helpful in settings where the efficacy of a drug does not always increase with the dose level. More information is available at these doses by backfilling patients at lower doses while the trial continues to explore higher doses. This paper proposes a Bayesian optimal interval design using efficacy and toxicity outcomes that allows patients to be backfilled at lower doses during a dose-finding trial while prioritizing the dose-escalation cohort to explore a higher dose. A simulation study shows that the proposed design, the BF-BOIN-ET design, has advantages compared to the other designs in terms of the percentage of correct OD selection, reducing the sample size, and shortening the duration of the trial in various realistic settings.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 2","pages":"e2470"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143597537","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}

引用次数: 0