Pharmaceutical Statistics最新文献_第6页

Robust Modestly Weighted Log-Rank Tests. 稳健适度加权对数秩检验。

IF 1.4 4区医学

Pharmaceutical Statistics Pub Date : 2026-01-01 DOI: 10.1002/pst.70066

Dominic Magirr, Fredrik Öhrn

{"title":"Robust Modestly Weighted Log-Rank Tests.","authors":"Dominic Magirr, Fredrik Öhrn","doi":"10.1002/pst.70066","DOIUrl":"10.1002/pst.70066","url":null,"abstract":"The introduction of checkpoint inhibitors in immuno-oncology has raised questions about the suitability of the log-rank test as the default primary analysis method in confirmatory studies, particularly when survival curves exhibit non-proportional hazards. The log-rank test, while effective in controlling false positive rates, may lose power in scenarios where survival curves remain similar for extended periods before diverging. To address this, various weighted versions of the log-rank test have been proposed, including the \"MaxCombo\" test, which combines multiple weighted log-rank statistics to enhance power across a range of alternative hypotheses. Despite its potential, the MaxCombo test has seen limited adoption, possibly owing to its proneness to produce counterintuitive results in situations where the hazard functions on the two arms cross. In response, the modestly weighted log-rank test was developed to provide a balanced approach, giving greater weight to later event times while avoiding undue influence from early detrimental effects. However, this test also faces limitations, particularly if the possibility of early separation of survival curves cannot be ruled out a priori. We propose a novel test statistic that integrates the strengths of the standard log-rank test, the modestly weighted log-rank test, and the MaxCombo test. By considering the maximum of the standard log-rank statistic and a modestly weighted log-rank statistic, the new test aims to maintain power under delayed effect scenarios while minimizing power loss relative to the log-rank test in worst-case scenarios. Simulation studies and a case study demonstrate the efficiency and robustness of this approach, highlighting its potential as a robust alternative for primary analysis in immuno-oncology trials.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 1","pages":"e70066"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145775451","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

Multiple Comparisons With Overdispersed Multinomial Data: Methods, Properties and Application. 过分散多项数据的多重比较：方法、性质及应用。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-01-01 DOI: 10.1002/pst.70073

Sören Budig, Charlotte Vogel, Frank Schaarschmidt

{"title":"Multiple Comparisons With Overdispersed Multinomial Data: Methods, Properties and Application.","authors":"Sören Budig, Charlotte Vogel, Frank Schaarschmidt","doi":"10.1002/pst.70073","DOIUrl":"10.1002/pst.70073","url":null,"abstract":"Overdispersion, a common issue in clustered multinomial data, can lead to biased standard errors and compromised statistical inference if not adequately addressed. This study describes a comprehensive procedure for constructing multiple comparisons of interest and applying multiplicity adjustments in the analysis of clustered, potentially overdispersed multinomial data. We investigate four quasi-likelihood estimators and the Dirichlet-multinomial model to account for overdispersion. Through a simulation study, we evaluate the performance of these methods under various scenarios, focusing on family-wise error rate, statistical power and coverage probability. Our findings indicate that the Afroz quasi-likelihood estimator is recommended when strict error control is required, whereas the Dirichlet-multinomial model is preferable when high statistical power is desired, albeit with a slightly increased tolerance for false positives. Additionally, we address the challenge of zero-count categories within groups, demonstrating that incorporating pseudo-observations can effectively mitigate associated estimation difficulties. Practical applications to real datasets from toxicology and flow cytometry underscore the robustness and practical utility of these methods.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 1","pages":"e70073"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12815589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003844","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}

引用次数: 0

Optimizing Dosing Strategies in Cell Therapy With Machine Learning and Exposure-Response Integration. 利用机器学习和暴露-反应集成优化细胞治疗的给药策略。

IF 1.4 4区医学

Pharmaceutical Statistics Pub Date : 2026-01-01 DOI: 10.1002/pst.70048

Shuqi Wang, Yunqi Zhao, Jia Li, Rachael Liu, Ganesh Mugundu

{"title":"Optimizing Dosing Strategies in Cell Therapy With Machine Learning and Exposure-Response Integration.","authors":"Shuqi Wang, Yunqi Zhao, Jia Li, Rachael Liu, Ganesh Mugundu","doi":"10.1002/pst.70048","DOIUrl":"10.1002/pst.70048","url":null,"abstract":"In cell therapy product development, cell expansion is highly correlated with response and safety. Significant heterogeneity in patient and product characteristics contributes to variability in cell expansion, persistence and response. Integrating exposure assessments enables us to use comprehensive information to make informed dose selection decisions, aligning with the expectations outlined in FDA's Project OPTIMUS. We propose a seamless phase I/II design that integrates data from toxicity, efficacy, cellular kinetics (CK), and baseline patient/product characteristics for optimal dose selection. Utilizing random forest (RF) with all available data, we guide dose escalation and subsequently narrow down the dose options. Additional patients are then randomly assigned to promising doses for further investigation. Throughout this process, interim analyses based on RF estimations are conducted to discontinue doses that are deemed futile or toxic. The optimal dose (OD) is ultimately chosen based on its safety profile and highest efficacy rate. The proposed RF-based seamless phase I/II design, incorporating exposure data, is entirely data-driven for dose determination. Simulation studies show that the proposed design has desirable operating characteristics, including high accuracy in selecting the optimal dose and effectively allocating patients to potentially therapeutic doses while minimizing exposure to toxic doses. On average, the RF-based algorithm selects 0-3 doses for further exploration.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 1","pages":"e70048"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145715348","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

Adaptive Constrained Weighted Estimation for Incorporating Multiple External Information Sources. 多外部信息源的自适应约束加权估计。

IF 1.4 4区医学

Pharmaceutical Statistics Pub Date : 2026-01-01 DOI: 10.1002/pst.70059

Keita Takahashi, Kazufumi Okada, Shiro Tanaka, Isao Yokota

{"title":"Adaptive Constrained Weighted Estimation for Incorporating Multiple External Information Sources.","authors":"Keita Takahashi, Kazufumi Okada, Shiro Tanaka, Isao Yokota","doi":"10.1002/pst.70059","DOIUrl":"10.1002/pst.70059","url":null,"abstract":"There is growing interest in hybrid control approaches that augment concurrent control data in clinical trials. This study proposes a frequentist method based on constrained weighted maximum likelihood estimation with a concept of effective sample size to borrow information from multiple external sources. The proposed method manages heterogeneity across information sources, while ensuring that the power and the Type I error rate are controlled precisely at nominal levels when all information sources are comparable. The method does not require tuning parameters to be prespecified; instead, the weights assigned to each external information source are dynamically optimized. This study evaluates the operating characteristics of the proposed method under various scenarios through simulations and demonstrates its weighting behavior by numerical examples and an application to clinical trial data. The simulation results show that the proposed method outperforms existing methods and demonstrates greater robustness in many situations. Furthermore, the numerical examples and application highlight its ability to achieve adaptive weighting while maintaining the effective sample size at a prespecified target level.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 1","pages":"e70059"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145654550","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

Nonparametric Inference for the Covariate-Adjusted Youden Index and Associated Cut-Off Points for Three Ordinal Diagnostic Groups. 三个顺序诊断组的协变量校正约登指数和相关分界点的非参数推断。

IF 1.4 4区医学

Pharmaceutical Statistics Pub Date : 2025-11-01 DOI: 10.1002/pst.70043

Asieh Maghami-Mehr, Hamzeh Torabi, Hossein Nadeb, Yichuan Zhao

引用次数: 0

Addressing Non-Exchangeability in Hybrid Control Studies: A Variable Selection Approach. 解决混合控制研究中的不可互换性：一种变量选择方法。

IF 1.4 4区医学

Pharmaceutical Statistics Pub Date : 2025-11-01 DOI: 10.1002/pst.70056

Zhiwei Zhang, Jialuo Liu, Peisong Han

{"title":"Addressing Non-Exchangeability in Hybrid Control Studies: A Variable Selection Approach.","authors":"Zhiwei Zhang, Jialuo Liu, Peisong Han","doi":"10.1002/pst.70056","DOIUrl":"10.1002/pst.70056","url":null,"abstract":"There is growing interest in a hybrid control design for treatment evaluation, where a randomized controlled trial is augmented with external control data from a previous trial or a real world data source. The hybrid control design has the potential to improve efficiency but also carries the risk of introducing bias. The potential bias in a hybrid control study can be mitigated by adjusting for baseline covariates that are related to the control outcome. A key assumption for this approach is that the internal and external control outcomes are exchangeable upon conditioning on a set of measured covariates. Possible violations of the exchangeability assumption can result in bias and thus need to be addressed systematically. This article proposes a variable selection approach to addressing non-exchangeability in hybrid control studies. Under a specified outcome regression model, possible non-exchangeability can be represented as interactions between covariates and an external control indicator, some of which may be null (with zero coefficients). Null interactions support information borrowing, while non-null interactions require adjustment. Identifying non-null interactions for inclusion in the model is a variable selection problem. The adaptive lasso can be used to perform variable selection and modeling fitting, and the fitted model can be substituted into a g-computation formula. Simulation results demonstrate that, under appropriate conditions, this approach is able to improve efficiency by incorporating external control data in the absence of full exchangeability.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70056"},"PeriodicalIF":1.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145534101","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

Order of Addition in Mixture-Amount Experiments. 混合量实验中的加成顺序。

IF 1.4 4区医学

Pharmaceutical Statistics Pub Date : 2025-11-01 DOI: 10.1002/pst.70047

Taha Hasan, Touqeer Ahmad

{"title":"Order of Addition in Mixture-Amount Experiments.","authors":"Taha Hasan, Touqeer Ahmad","doi":"10.1002/pst.70047","DOIUrl":"10.1002/pst.70047","url":null,"abstract":"In a mixture experiment, we study the behavior and properties of m mixture components, where the primary focus is on the proportions of the components that make up the mixture rather than the total amount. Mixture-amount experiments are specialized types of mixture experiments where both the proportions of the components in the mixture and the total amount of the mixture are of interest. In this paper, we consider an Order-of-Addition (OofA) mixture-amount experiment in which the response depends on both the mixture amounts of components and their order of addition. Full mixture OofA designs are constructed to maintain orthogonality between the mixture-amount model terms and the effects of the order of addition. But the number of runs in such full OofA designs increases as m increases. We employ the Threshold Accepting (TA) Algorithm to select an n-row subset from the full OofA mixture design that maximizes G-optimality while minimizing the number of experimental runs. Further, the G-efficiency criterion is used to assess how well the design supports the precise and unbiased estimation of the model parameters. These designs enable the estimation of mixture-component model parameters and the order-of-addition effects. The Fraction of Design Space (FDS) plot is used to provide a visual assessment of the prediction capabilities of a design across the entire design space.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70047"},"PeriodicalIF":1.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12594051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145459481","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}

引用次数: 0

BOIN-MEM: A Two-Stage Design for Dose Optimization With Information Borrowing Across Dose Levels and Stages in Oncology Phase I/II Trials. BOIN-MEM：肿瘤I/II期临床试验中剂量水平和阶段信息借鉴的两阶段剂量优化设计

IF 1.4 4区医学

Pharmaceutical Statistics Pub Date : 2025-11-01 DOI: 10.1002/pst.70045

Ryo Kitabayashi, Kentaro Takeda, Hiroyuki Sato, Akihiro Hirakawa

{"title":"BOIN-MEM: A Two-Stage Design for Dose Optimization With Information Borrowing Across Dose Levels and Stages in Oncology Phase I/II Trials.","authors":"Ryo Kitabayashi, Kentaro Takeda, Hiroyuki Sato, Akihiro Hirakawa","doi":"10.1002/pst.70045","DOIUrl":"10.1002/pst.70045","url":null,"abstract":"The launch of Project Optimus has shifted a paradigm in oncology early-phase trials toward identifying the optimal biological dose (OBD), considering both the drug's toxicity and efficacy. Conventional two-stage designs for dose optimization in phase I/II trials typically identify tolerable dose levels in Stage 1, followed by selecting the OBD based on efficacy at expansion cohorts in Stage 2. However, these designs could be inefficient because toxicity and efficacy data from patients in Stage 1 treated at dose levels excluded from Stage 2 are not explicitly used in the final OBD determination. To address this issue, we developed a novel two-stage design for dose optimization in oncology phase I/II trials. This design, termed BOIN-MEM, leverages Bayesian optimal interval design (BOIN) for Stage 1 and extends multi-source exchangeability models (MEM) to facilitate information borrowing across dose levels and stages when selecting the OBD at the end of Stage 2. This information borrowing strategy is based on utility for each dose level, which is a measure representing a drug's clinical value, calculated based on the toxicity and efficacy outcomes, and their pre-specified scores. BOIN-MEM allows for the estimation of mean utility, considering heterogeneity not only between dose levels but also between stages due to variations in the target population. Through comprehensive simulation studies with practical scenarios of dose-toxicity and dose-efficacy relationships, BOIN-MEM demonstrated higher accuracy of OBD selection and reduced patient exposure to overdoses compared with existing designs under various scenarios.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70045"},"PeriodicalIF":1.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145308790","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

Improving Maximum Tolerated Dose Selection in Model-Assisted Designs for Phase I Trials Through Bayesian Dose-Response Model. 通过贝叶斯剂量-反应模型改进I期试验模型辅助设计中的最大耐受剂量选择。

IF 1.4 4区医学

Pharmaceutical Statistics Pub Date : 2025-11-01 DOI: 10.1002/pst.70046

Rentaro Wakayama, Tomotaka Momozaki, Shuji Ando

{"title":"Improving Maximum Tolerated Dose Selection in Model-Assisted Designs for Phase I Trials Through Bayesian Dose-Response Model.","authors":"Rentaro Wakayama, Tomotaka Momozaki, Shuji Ando","doi":"10.1002/pst.70046","DOIUrl":"10.1002/pst.70046","url":null,"abstract":"Model-assisted designs have garnered significant attention in recent years due to their high accuracy in identifying the maximum tolerated dose (MTD) and their operational simplicity. To identify the MTD, they employ estimated dose limiting toxicity (DLT) probabilities via isotonic regression with pool-adjacent violators algorithm (PAVA) after trials have been completed. PAVA adjusts independently estimated DLT probabilities with the Bayesian binomial model at each dose level using posterior variances ensure the monotonicity that toxicity increases with dose. However, in small sample settings such as Phase I oncology trials, this approach can lead to unstable DLT probability estimates and reduce MTD selection accuracy. To address this problem, we propose a novel MTD identification strategy in model-assisted designs that leverages a Bayesian dose-response model. Employing the dose-response model allows for stable estimation of the DLT probabilities under the monotonicity by borrowing information across dose levels, leading to an improvement in MTD identification accuracy. We discuss the specification of prior distributions that can incorporate information from similar trials or the absence of such information. We examine dose-response models employing logit, log-log, and complementary log-log link functions to assess the impact of link function differences on the accuracy of MTD selection. Through extensive simulations, we demonstrate that the proposed approach improves MTD selection accuracy by more than 10% in some scenarios and by approximately 6% on average compared to conventional approach. These findings indicate that the proposed approach can contribute to further enhancing the efficiency of Phase I oncology trials.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 6","pages":"e70046"},"PeriodicalIF":1.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12553356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145368517","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}

引用次数: 0

Adjustment for Inconsistency in Adaptive Phase 2/3 Designs With Dose Optimization. 剂量优化自适应2/3阶段设计中的不一致性调整

IF 1.4 4区医学

Pharmaceutical Statistics Pub Date : 2025-11-01 DOI: 10.1002/pst.70031

Cong Chen, Mo Huang, Xuekui Zhang

引用次数: 0