Journal of Biopharmaceutical Statistics最新文献_第2页

A composite semiparametric homogeneity test for the distributions of multigroup interval-bounded longitudinal data. 多组区间有界纵向数据分布的复合半参数齐性检验。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-02 Epub Date: 2023-11-15 DOI: 10.1080/10543406.2023.2275769

Zhanfeng Wang, Wenmei Li, Hao Ding, Dongsheng Tu

{"title":"A composite semiparametric homogeneity test for the distributions of multigroup interval-bounded longitudinal data.","authors":"Zhanfeng Wang, Wenmei Li, Hao Ding, Dongsheng Tu","doi":"10.1080/10543406.2023.2275769","DOIUrl":"10.1080/10543406.2023.2275769","url":null,"abstract":"Motivated by comparing the distribution of longitudinal quality of life (QoL) data among different treatment groups from a cancer clinical trial, we propose a semiparametric test statistic for the homogeneity of the distributions of multigroup longitudinal measurements, which are bounded in a closed interval with excess observations taking the boundary values. Our procedure is based on a three-component mixed density ratio model and a composite empirical likelihood for the longitudinal data taking values inside the interval. A nonparametric bootstrap method is applied to calculate the p-value of the proposed test. Simulation studies are conducted to evaluate the proposed procedure, which show that the proposed test is effective in controlling type I errors and more powerful than the procedure which ignores the values on the boundaries. It is also robust to the model mispecification than the parametric test. The proposed procedure is also applied to compare the distributions of the scores of Physical Function subscale and Global Heath Status between the patients randomized to two treatment groups in a cancer clinical trial.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"58-69"},"PeriodicalIF":1.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134650477","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

Adjusted win ratio using the inverse probability of treatment weighting. 使用治疗加权的逆概率调整获胜率。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-02 Epub Date: 2023-11-10 DOI: 10.1080/10543406.2023.2275759

Duolao Wang, Sirui Zheng, Ying Cui, Nengjie He, Tao Chen, Bo Huang

{"title":"Adjusted win ratio using the inverse probability of treatment weighting.","authors":"Duolao Wang, Sirui Zheng, Ying Cui, Nengjie He, Tao Chen, Bo Huang","doi":"10.1080/10543406.2023.2275759","DOIUrl":"10.1080/10543406.2023.2275759","url":null,"abstract":"The win ratio method has been increasingly applied in the design and analysis of clinical trials. However, the win ratio method is a univariate approach that does not allow for adjusting for baseline imbalances in covariates, although a stratified win ratio can be calculated when the number of strata is small. This paper proposes an adjusted win ratio to control for such imbalances by inverse probability of treatment weighting (IPTW) method. We derive the adjusted win ratio with its variance and suggest three IPTW adjustments: IPTW-average treatment effect (IPTW-ATE), stabilized IPTW-ATE (SIPTW-ATE) and IPTW-average treatment effect in the treated (IPTW-ATT). The proposed adjusted methods are applied to analyse a composite outcome in the CHARM trial. The statistical properties of the methods are assessed through simulations. Results show that adjusted win ratio methods can correct the win ratio for covariate imbalances at baseline. Simulation results show that the three proposed adjusted win ratios have similar power to detect the treatment difference and have slightly lower power than the corresponding adjusted Cox models when the assumption of proportional hazards holds true but have consistently higher power than adjusted Cox models when the proportional hazard assumption is violated.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"21-36"},"PeriodicalIF":1.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016204","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

Data-driven monitoring for phase II clinical trial designs based on percentile event time test. 基于百分位事件时间测试的数据驱动型 II 期临床试验设计监测。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-02 Epub Date: 2023-12-22 DOI: 10.1080/10543406.2023.2292209

Yeonhee Park, Zhanpeng Xu

引用次数: 0

MIDAS-2: an enhanced Bayesian platform design for immunotherapy combinations with subgroup efficacy exploration. MIDAS-2：增强型贝叶斯平台设计，用于亚组疗效探索的免疫疗法组合。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-02 Epub Date: 2023-12-22 DOI: 10.1080/10543406.2023.2292211

Liwen Su, Xin Chen, Jingyi Zhang, Fangrong Yan

{"title":"MIDAS-2: an enhanced Bayesian platform design for immunotherapy combinations with subgroup efficacy exploration.","authors":"Liwen Su, Xin Chen, Jingyi Zhang, Fangrong Yan","doi":"10.1080/10543406.2023.2292211","DOIUrl":"10.1080/10543406.2023.2292211","url":null,"abstract":"Although immunotherapy combinations have revolutionised cancer treatment, the rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging. This necessitates innovative, integrated, and efficient trial designs. In this study, we extend the MIDAS design to include subgroup exploration and propose an enhanced Bayesian information borrowing platform design called MIDAS-2. MIDAS-2 enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We use a regression model to characterize the efficacy pattern in subgroups. Information borrowing is applied through Bayesian hierarchical modelling to improve trial efficiency considering the limited sample size in subgroups. Time trend calibration is also employed to avoid potential baseline drifts. Simulation results demonstrate that MIDAS-2 yields high probabilities for identifying the effective drug combinations as well as promising subgroups, facilitating appropriate selection of the best treatments for each subgroup. The proposed design is robust against small time trend drifts, and the type I error is successfully controlled after calibration when a large drift is expected. Overall, MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"37-57"},"PeriodicalIF":1.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138833056","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

Interval estimation of common risk difference for stratified unilateral and bilateral data✩. 分层单侧和双侧数据的共同风险差异区间估计✩。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-02 Epub Date: 2024-01-05 DOI: 10.1080/10543406.2023.2296062

Shuman Sun, Zhiming Li, Keyi Mou

引用次数: 0

Novel 3-arm wait-list controlled trial designs together with mixed-effects analysis improve precision of treatment effect estimators. 新的三臂等待列表对照试验设计与混合效应分析一起提高了治疗效果估计的精度。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-02 Epub Date: 2023-11-06 DOI: 10.1080/10543406.2023.2275755

Xiangmei Ma, Yin Bun Cheung

{"title":"Novel 3-arm wait-list controlled trial designs together with mixed-effects analysis improve precision of treatment effect estimators.","authors":"Xiangmei Ma, Yin Bun Cheung","doi":"10.1080/10543406.2023.2275755","DOIUrl":"10.1080/10543406.2023.2275755","url":null,"abstract":"Clinical trialists have long been searching for approaches to increase statistical power without increasing sample size. Conventional wait-list controlled (WLC) trials are limited to two trial arms and two or three repeated measurements per person. These features limit statistical power. Furthermore, their analysis is usually based on analysis of covariance or mixed effects modelling, with a focus on estimating treatment effect at one time-period after initiation of therapy. We propose two 3-arm WLC trial designs together with a mixed-effects analysis framework. The designs require three or four repeated measurements per person. The analytic framework defines up to three treatment effect estimands, representing the effects at one to three time-periods after initiation of therapy. The precision (inverse of variance) of the treatment effect estimators in the new and conventional trial designs are analytically derived and evaluated in simulations. The results are interpreted in the context of a cognitive training trial in older people. The proposed designs and analysis methods increase the precision level of treatment effect estimators as compared to conventional designs and analyses. Given a target level of statistical power, the proposed methods require a smaller number of participants per trial than the conventional methods, without necessarily increasing the number of measurements per trial. Furthermore, the proposed analytic framework sheds light on the treatment effects at different times after initiation of therapy, which is not usually considered in conventional WLC trial analysis. In situations that a WLC trial is appropriate, the 3-arm designs are useful alternatives to existing 2-arm designs.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"70-84"},"PeriodicalIF":1.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71489092","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

Generalized triple outcome decision-making in basket trials. 篮子试验中的通用三重结果决策。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-02 Epub Date: 2024-01-02 DOI: 10.1080/10543406.2023.2296054

Miao Zang, Rui Liu

引用次数: 0

Enhancement of Bayesian optimal interval design by accounting for overdose and underdose errors trade-offs. 考虑过量和剂量不足误差权衡的贝叶斯最优间隔设计改进。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-01-02 Epub Date: 2023-11-15 DOI: 10.1080/10543406.2023.2275766

Ryo Sadachi, Hiroyuki Sato, Takeo Fujiwara, Akihiro Hirakawa

{"title":"Enhancement of Bayesian optimal interval design by accounting for overdose and underdose errors trade-offs.","authors":"Ryo Sadachi, Hiroyuki Sato, Takeo Fujiwara, Akihiro Hirakawa","doi":"10.1080/10543406.2023.2275766","DOIUrl":"10.1080/10543406.2023.2275766","url":null,"abstract":"Model-assisted designs, a new class of dose-finding designs for determining the maximum tolerated dose (MTD), model only the dose-limiting toxicity (DLT) data observed at the current dose based on a simple binomial model and offer the boundaries of DLT for the determination of dose escalation, retention, or de-escalation before beginning the trials. The boundaries for dose-escalation and de-escalation decisions are relevant to the operating characteristics of the design. The well-known model-assisted design, Bayesian Optimal Interval (BOIN), selects these boundaries to minimize the probability of incorrect decisions at each dose allocation but does not distinguish between overdose and underdose allocations caused by incorrect decisions when calculating the probability of incorrect decisions. Distinguishing between overdose and underdose based on the decision error in the BOIN design is expected to increase the accuracy of MTD determination. In this study, we extended the BOIN design to account for the decision probabilities of incorrect overdose and underdose allocations separately. To minimize the two probabilities simultaneously, we propose utilizing multiple objective optimizations and formulating an approach for determining the boundaries for dose escalation and de-escalation. Comprehensive simulation studies using fixed and randomly generated scenarios of DLT probability demonstrated that the proposed method is superior or comparable to existing interval designs, along with notably better operating characteristics of the proposed method.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"107592749","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

The effect of misclassification on sample size for one and two-sample tests with binary endpoints. 双终点单样本和双样本检验中误分类对样本量的影响。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-12-26 DOI: 10.1080/10543406.2024.2444231

Péter Hársfalvi, Jenő Reiczigel

{"title":"The effect of misclassification on sample size for one and two-sample tests with binary endpoints.","authors":"Péter Hársfalvi, Jenő Reiczigel","doi":"10.1080/10543406.2024.2444231","DOIUrl":"https://doi.org/10.1080/10543406.2024.2444231","url":null,"abstract":"In recent years, an increasing number of publications on the analysis of binary data have applied methods that take misclassification into account. However, potential misclassification is often ignored in study design due to the lack of sample size formulas or software. This may lead to a considerable loss of power in studies that only account for misclassification at the analysis stage. We argue that analyses correcting for misclassification should be used in combination with appropriate sample size adjustment in the design phase of the studies. We illustrate the importance of this by comparing the required sample sizes with and without misclassification, and provide an appropriate sample size procedure implemented as an R function for the one-sample and two-sample tests for binary endpoints. The sample size is calculated from the presumed binomial parameters (p0 and pa for one-sample and p1 and p2 for two-sample tests), the required power, and the probabilities of correct classification, sensitivity (Se), and specificity (Sp). Our results show that misclassification may drastically affect the necessary sample size in both testing scenarios.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-12"},"PeriodicalIF":1.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900512","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

Valid overall odds ratio estimators using different stratified sampling schemes. 使用不同分层抽样方案的有效总体优势比估计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-12-26 DOI: 10.1080/10543406.2024.2444232

Hani M Samawi, Jing Kersey

{"title":"Valid overall odds ratio estimators using different stratified sampling schemes.","authors":"Hani M Samawi, Jing Kersey","doi":"10.1080/10543406.2024.2444232","DOIUrl":"https://doi.org/10.1080/10543406.2024.2444232","url":null,"abstract":"The study presents valid estimators for determining the overall odds ratio between two independent groups within stratified populations, utilizing both simple stratified sampling (SSRS) and stratified ranked set sampling (SRSS) methodologies. Through analytical derivations, we establish the expected values and variances for these estimators. Two distinct types of estimators namely, the naive weighted and the Cochran Mantel-Haenszel-Haenszel approaches are thoroughly examined. Our investigation encompasses an in-depth analysis of the expectation and variance of these estimators, shedding light on their performance characteristics. Through intensive simulation experiments, we discern that estimators based on SRSS exhibit notable advantages over their SSRS counterparts. To validate the efficacy of our proposed estimators, we conduct an empirical assessment utilizing data from the (2009-2010) National Health and Nutrition Examination Survey (NHANES). Through this analysis, we glean insights into the performance of the estimators in a real-world context. In summary, our study contributes valuable insights into the estimation of the overall odds ratio within stratified populations. By comparing SSRS and SRSS methodologies and evaluating different estimation approaches, we provide researchers with robust tools for analyzing odds ratios in diverse settings. Moreover, our empirical validation using NHANES data underscores the practical utility of the proposed estimators in real-world applications.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900513","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