Journal of Biopharmaceutical Statistics最新文献

Implementation of the ICH E9 (R1) addendum in vaccine efficacy studies: the hypothetical and principal stratum strategies. 在疫苗效力研究中实施ICH E9 （R1）附录：假设和主要阶层策略。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-10-02 DOI: 10.1080/10543406.2025.2547588

Silvia Noirjean, Daniele Bottigliengo, Elisa Cinconze, Ali Charkhi, Toufik Zahaf, Fan Li, Andrea Callegaro

引用次数: 0

On improving the accuracy of prediction in Cox models for failure times using copulas. 利用copula提高Cox模型失效时间预测的准确性。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-09-19 DOI: 10.1080/10543406.2025.2557573

Xiaofeng Liu, Ayyub Sheikhi

引用次数: 0

BOP2-FE: Bayesian optimal phase II design with futility and efficacy-stopping boundaries. BOP2-FE：具有无效和有效性停止边界的贝叶斯最优II期设计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-09-17 DOI: 10.1080/10543406.2025.2558142

Xinling Xu, Atsuki Hashimoto, Belay B Yimer, Kentaro Takeda

引用次数: 0

Linear regression models for analyzing the covariate-adjusted Youden index and associated cut-off points in three diagnostic groups. 用线性回归模型分析三个诊断组的协变量调整约登指数和相关截断点。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-09-17 DOI: 10.1080/10543406.2025.2558141

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

引用次数: 0

Sample size reduction in preclinical experiments: A Bayesian sequential decision-making framework. 临床前实验的样本量减少：贝叶斯顺序决策框架。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-09-10 DOI: 10.1080/10543406.2025.2556680

Jizhou Kang, Theodoro Koulis, Tony Pourmohamad

{"title":"Sample size reduction in preclinical experiments: A Bayesian sequential decision-making framework.","authors":"Jizhou Kang, Theodoro Koulis, Tony Pourmohamad","doi":"10.1080/10543406.2025.2556680","DOIUrl":"https://doi.org/10.1080/10543406.2025.2556680","url":null,"abstract":"When animals are used in a preclinical experiment, ethical concerns may arise regarding animal welfare. The 3Rs principles were developed to guide more humane animal research practices. This article specifically addresses the reduction aspect of the 3Rs. Under our proposed framework, the preclinical experiment is conducted sequentially, and at every stage of the experiment we examine the outcome and decide whether to stop early for efficacy or futility. Compared to traditional methods in the literature, which typically only check for efficacy, the proposed method has the potential to further reduce the number of animals needed in an experiment. The proposed design requires specifying loss functions at every stage of the experiment. These functions may be directly related to the actual cost of conducting the study or can be calibrated to reflect the prior belief that the drug will be effective. Decisions are made based on minimizing the posterior expected loss. We evaluate the design methodology through simulation studies that involve two-arm experiments with either binary or continuous endpoints. Additionally, we also provide examples taken from real preclinical experiments.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034648","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

Comparison of parametric and hybrid methods for estimating mean survival time in clinical study. 临床研究中估计平均生存时间的参数和混合方法的比较。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-09-09 DOI: 10.1080/10543406.2025.2557539

Yuki Nakagawa, Takashi Sozu

{"title":"Comparison of parametric and hybrid methods for estimating mean survival time in clinical study.","authors":"Yuki Nakagawa, Takashi Sozu","doi":"10.1080/10543406.2025.2557539","DOIUrl":"https://doi.org/10.1080/10543406.2025.2557539","url":null,"abstract":"The mean survival time (MST) is usually estimated as the area under the curve of the estimated survival function obtained using the Kaplan-Meier method. However, when the maximum observed survival time is censored, the MST cannot be estimated because the survival function does not reach zero. In such cases, parametric and hybrid methods are used to estimate the MST. The parametric method assumes a probability distribution throughout the entire time and has been evaluated in several studies. The hybrid method combines two approaches: it first applies the Kaplan-Meier method up to a specified time point and then extrapolates the survival curve beyond this point using a parametric distribution. Evaluation of the performance of the hybrid method is limited to a few data-generating mechanisms and analysis models. This study evaluated the performance of the parametric and hybrid methods through numerical experiments, assuming nine probability distributions for the data-generating mechanism and 16 analysis models. The bias and root mean square error of the generalized gamma model and the Royston-Parmar models with the log(-log) link function tended to be smaller than those of the other analysis models, even when the assumed probability distribution of the analysis model was inconsistent with that of the data-generating mechanism when the sample size is relatively large. Overall, the performances of the parametric and hybrid methods were comparable across all the data-generating mechanisms.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-10"},"PeriodicalIF":1.2,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024796","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

A bias correction method for hazard ratio estimation and its inference in a multiple-arm clinical trial. 多组临床试验中风险比估计及其推断的偏倚校正方法。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-09-09 DOI: 10.1080/10543406.2025.2547590

Liji Shen, Ziwen Wei, Xuan Deng

{"title":"A bias correction method for hazard ratio estimation and its inference in a multiple-arm clinical trial.","authors":"Liji Shen, Ziwen Wei, Xuan Deng","doi":"10.1080/10543406.2025.2547590","DOIUrl":"https://doi.org/10.1080/10543406.2025.2547590","url":null,"abstract":"A randomized clinical trial with multiple experimental groups and one common control group is often used to speed up development to select the best experimental regimen or to increase the chance of success of clinical trials. Most of the time, multiple dose levels of an experimental drug or multiple combinations of one experimental drug with other drugs comprise multiple experimental groups. Because the experimental drug appears in multiple comparisons with a shared control group, multiple testing adjustments to control the family-wise type I error rate are needed. We extend the stepwise over-correction (SOC) method that is applied to a multi-arm trial with a response rate as its endpoint to a multi-arm trial where time to event is the primary endpoint and confidence interval of the hazard ratio determines the statistical significance. We provide the formula of the bias of the maximum treatment effect estimate toward the true treatment effect between the selected experimental group and the shared control group. We aim to use the bias-corrected estimate for the inference of treatment effects in multi-arm trials on the full alpha level and demonstrate a completely new type of reject region. This approach does not require us to split alpha level among the multiple comparisons or to specify the test order ahead of time. The type I error control and the power enhancement of the proposed approach are both held.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024748","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

Correction. 修正。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-09-04 DOI: 10.1080/10543406.2025.2557043

引用次数: 0

Mitigating propensity score model misspecification with multiply robust weights when leveraging external data. 利用外部数据时，使用多个鲁棒权重减轻倾向评分模型的错误说明。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-08-28 DOI: 10.1080/10543406.2025.2547593

Jinmei Chen, Guoyou Qin, Yongfu Yu

{"title":"Mitigating propensity score model misspecification with multiply robust weights when leveraging external data.","authors":"Jinmei Chen, Guoyou Qin, Yongfu Yu","doi":"10.1080/10543406.2025.2547593","DOIUrl":"https://doi.org/10.1080/10543406.2025.2547593","url":null,"abstract":"Propensity score-integrated Bayesian dynamic borrowing methods offer an effective approach for covariate adjustment when using external data to augment randomized controlled trials (RCTs). However, identifying the correct propensity score model can be challenging due to unknown treatment selection processes, potentially leading to model misspecification and biased estimates. To improve robustness to model misspecification, we propose an innovative Bayesian inference procedure that incorporates multiply robust weights into the construction of informative power priors. Specifically, we specify a set of candidate propensity score models to derive multiply robust weights, balancing covariates between the current data and external data. The weighted external data is then incorporated into the analysis using a Bayesian power prior method. We further extend this approach to leverage multiple external datasets. Simulation studies indicate that when the set of postulated propensity score models include a correctly specified model, the proposed method achieves desirable operating characteristics, including low bias, low root mean squared error (RMSE), controlled type I error rate at the predetermined nominal level, and high statistical power. This method also provides a robust strategy for researchers who may have a difficult time developing or selecting a single propensity score model.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-14"},"PeriodicalIF":1.2,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979522","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

Application of marginal structural models for causal inference on the treatment effect for overall survival in randomized controlled trials with control arm patients switching to active intervention after disease progression. 应用边际结构模型对随机对照试验中治疗效果对总生存率的因果推断，对照组患者在疾病进展后转为积极干预。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-08-26 DOI: 10.1080/10543406.2025.2547591

Jing Xu, Camden Bay, Bingxia Wang, Guohui Liu, Cong Li

{"title":"Application of marginal structural models for causal inference on the treatment effect for overall survival in randomized controlled trials with control arm patients switching to active intervention after disease progression.","authors":"Jing Xu, Camden Bay, Bingxia Wang, Guohui Liu, Cong Li","doi":"10.1080/10543406.2025.2547591","DOIUrl":"10.1080/10543406.2025.2547591","url":null,"abstract":"This research explores the application of marginal structural models (MSMs) in evaluating the causal treatment effect of active intervention versus control on overall survival in randomized clinical trials (RCTs) allowing for control arm patients to switch to active intervention after disease progression. When MSMs are applied in RCTs under this type of treatment switching setting, the question of interest and model specifications differ from both observational studies and from RCTs where patients in both arms are permitted to take alternative treatments after disease progression. A violation of structural positivity may result as an undesired consequence if MSM model weights are constructed using data directly from both arms. This research proposes a two-step approach to avoid this issue. Through simulation studies, it is demonstrated that the proposed approach allows for MSM to be used for analyzing survival data to detect causal active treatment effects under this one-way treatment switching setting. Additionally, estimation for the causal effect of the active intervention as the next line (post-disease progression) therapy can also be obtained from the MSM approach. A case study is presented to illustrate the application of MSMs under this type of treatment switching setting.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-18"},"PeriodicalIF":1.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979493","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