Journal of Biopharmaceutical Statistics最新文献_第8页

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 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub 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":"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":"1051-1064"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","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

Characterization of a credibility index. 可信度指数的特征。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-02-09 DOI: 10.1080/10543406.2025.2456170

Piero Quatto, Enrico Ripamonti, Donata Marasini

{"title":"Characterization of a credibility index.","authors":"Piero Quatto, Enrico Ripamonti, Donata Marasini","doi":"10.1080/10543406.2025.2456170","DOIUrl":"10.1080/10543406.2025.2456170","url":null,"abstract":"In recent years, the role of the p-value in applied research has been heavily scrutinized. Several new proposals have been put forward from a Bayesian viewpoint, including the analysis of credibility. By using the reverse Bayes theorem, and reasoning in terms of subverting the significance or the non-significance denoted by the p-value, this analysis provides the credibility, in a Bayesian sense, of an experimental result. We discuss a normalized indicator of credibility, namely <math><mi>C</mi></math>, a variant of the index <math><mover><mi>C</mi><mo>˜</mo></mover></math> (Quatto et al. J. Biopharm. Stat. 32, 308-329, 2022). This can be used to assess the degree of credibility of experimental results and can also be compared with a fixed threshold. The index is extended to the case of one-sided hypotheses. A simulation study is conducted to empirically assess the behavior of the index <math><mi>C</mi></math>. Two illustrative examples in the contexts of pharmacotherapy for COVID-19 and heart failure are presented. We then propose adopting the credibility index for meta-analyses, in which it can provide a suitable diagnostic value for modeling fixed and random effects.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"854-870"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384139","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

On F_β -score for medical diagnostics tests of binary diseases: proposing new measures of accuracy. 二元疾病医学诊断试验的Fβ评分：提出新的准确性测量方法。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-02-27 DOI: 10.1080/10543406.2025.2469866

Marwan Alsharman, Hani Samawi, Jing Kersey, Divine Wanduku

{"title":"On Fβ -score for medical diagnostics tests of binary diseases: proposing new measures of accuracy.","authors":"Marwan Alsharman, Hani Samawi, Jing Kersey, Divine Wanduku","doi":"10.1080/10543406.2025.2469866","DOIUrl":"10.1080/10543406.2025.2469866","url":null,"abstract":"Accurate differentiation between health states - diseased or non-diseased - is essential in clinical diagnostics. Optimal cut-off points, or thresholds used to classify test results, are crucial for precise diagnoses. This work introduces the Harmonic Mean of F-score and inverse F-score (HF), a novel metric for a balanced assessment of diagnostic accuracy. HF integrates Specificity (Sp) and Negative Predictive Value (NPV) into the Negative F-score (NFγ), ensuring a comprehensive evaluation of true negatives and negative test reliability. Prioritizing both true positives and true negatives, HF was used in optimal cut-off point estimation under binary disease classification. Simulation results revealed that the HF measure performed well, often surpassing established methods in specific settings. The HF measure and cut-off point selection criterion were applied to real-life data, showcasing its ability to provide a balanced evaluation of diagnostic accuracy. The HF measure frequently outperformed traditional metrics. The HF metric's flexibility, allowing parameter adjustments to accommodate diverse scenarios, enables researchers and clinicians to tailor its emphasis on specific aspects of diagnostic performance depending on the context.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"900-926"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525256","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

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 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub 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

Bayesian network meta-regression for aggregate ordinal outcomes with imprecise categories. 具有不精确分类的累计有序结果的贝叶斯网络元回归。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-10-21 DOI: 10.1080/10543406.2025.2547585

Yeongjin Gwon, Ming-Hui Chen, May Mo, Xun Jiang, H Amy Xia, Joseph G Ibrahim

{"title":"Bayesian network meta-regression for aggregate ordinal outcomes with imprecise categories.","authors":"Yeongjin Gwon, Ming-Hui Chen, May Mo, Xun Jiang, H Amy Xia, Joseph G Ibrahim","doi":"10.1080/10543406.2025.2547585","DOIUrl":"10.1080/10543406.2025.2547585","url":null,"abstract":"Comparing emerging treatment options is often challenging because of the sparseness of direct comparisons from head-to-head trials and inconsistencies in outcome measures among published placebo-controlled trials for each treatment. One potential solution is to aggregate the different outcome measures into a single ordinal response variable for consistent evaluation. The ordinal response variable will inevitably contain unknown response categories because they cannot be directly derived from published data in the literature. In this paper, we propose a statistical methodology to overcome such a common but unresolved issue in the context of network meta-regression for aggregate ordinal outcomes. Specifically, we introduce unobserved latent counts and model these counts within a Bayesian framework. The proposed approach includes several existing models as special cases and also allows us to conduct a proper statistical analysis in the presence of trials with certain missing categories. We then develop an efficient Markov chain Monte Carlo sampling algorithm to carry out Bayesian computation. Variations of the deviance information criterion and widely applicable information criterion are used for the assessment of goodness-of-fit under different distributions of the latent counts. A case study demonstrating the usefulness of the proposed methodology is conducted using aggregate ordinal outcome data from 18 clinical trials in treating Crohn's Disease.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"995-1016"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145338245","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

Improved confidence estimation for the binomial proportion with applications to clinical studies. 改进的二项比例置信度估计及其在临床研究中的应用。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2023-03-16 DOI: 10.1080/10543406.2023.2183506

Mo Yang, Xiaoning Kang, Borek Puza

引用次数: 0

Bayesian Hierarchy model for population pharmacokinetics of amikacin in Japanese clinical population. 日本临床人群阿米卡星群体药代动力学的贝叶斯层次模型。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2024-12-31 DOI: 10.1080/10543406.2024.2444222

Ziyue Zhou, Guodong Li, Zhaosi Xu, Liping Zhu

{"title":"Bayesian Hierarchy model for population pharmacokinetics of amikacin in Japanese clinical population.","authors":"Ziyue Zhou, Guodong Li, Zhaosi Xu, Liping Zhu","doi":"10.1080/10543406.2024.2444222","DOIUrl":"10.1080/10543406.2024.2444222","url":null,"abstract":"Amikacin is one of the aminoglycosides with a narrow therapeutic window, significant dose-response relationship, and substantial interindividual pharmacokinetics (PK) variability, thus requiring an individualized dosing regimen. In this paper, a three-stage Bayesian hierarchical model was developed based on the known the PK parameters of amikacin obtained from a nonlinear mixed-effects model established for the Japanese population, and the weights were assigned to the priori and posteriori parts before two-dimensional Gibbs sampling, and simulations were performed using the data of 24 elderly patients with respiratory tract infections in Japan, after analyzing the predicted values and the range between effective trough concentrations (<math><mrow><msub><mi>C</mi><mrow><mi>trough</mi></mrow></msub></mrow></math>) and peak concentrations (<math><mrow><msub><mi>C</mi><mrow><mi>peak</mi></mrow></msub></mrow></math>), and residual plots, the dose for patients 3, 7, 9, and 16 was increased to 600 mg/day, and the dose for patient 20 was decreased to 400 mg/day, while keeping the remaining patients' doses unchanged, and the serum concentration at the time of the last administration was predicted, which showed that the Bayesian hierarchical model and the Markov chain Monte Carlo algorithm in this study performed well.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":"36 5","pages":"765-780"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148801828","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

Bayesian method for comparing F1 scores in the absence of a gold standard. 在没有金标准的情况下比较F1分数的贝叶斯方法。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2025-01-26 DOI: 10.1080/10543406.2025.2450319

Jun Tamura, Yusuke Saigusa, Junichi Fujita, Kouji Yamamoto

引用次数: 0

Whole-cage randomization for animal studies with unequal cage or group sizes. 全笼随机化，用于不同笼或组大小的动物研究。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub Date: 2023-09-19 DOI: 10.1080/10543406.2023.2256834

Tianhui Zhang, Benjamin Phillips, Natasha Karp, Junmin Wang, Steven Novick

{"title":"Whole-cage randomization for animal studies with unequal cage or group sizes.","authors":"Tianhui Zhang, Benjamin Phillips, Natasha Karp, Junmin Wang, Steven Novick","doi":"10.1080/10543406.2023.2256834","DOIUrl":"10.1080/10543406.2023.2256834","url":null,"abstract":"Following good statistical practice, in vivo study investigators allocate animals into two or more treatment groups using a randomization routine to eliminate selection bias and balance known and unknown confounding factors. For some studies, however, randomization at the individual animal level cannot be implemented. For example, for studies that involve co-housed male mice, an animal-level randomization can place unfamiliar mice together in the same cage, which can trigger fighting. To meet the ethical obligations to enhance the welfare of an animal used in science, the experimental procedures are, therefore, often modified, and male mice, possibly from the same brood, may be housed together. It follows that animal allocation into groups must proceed at the whole-cage level. Given the small sample sizes in animal studies, controlling baseline variables can be quite challenging. The difficulty greatly increases with a whole-cage randomization restriction. When the number of animals per cage or the treatment group sizes are unequal, there is no algorithm in the literature to perform the task. We propose a novel, fast, and reliable algorithm to provide a whole-cage randomization that balances one or more baseline variables across groups. The algorithm was applied to a realistic example dataset.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"742-752"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41179421","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 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-08-01 Epub 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":"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":"781-800"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","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