Journal of Biopharmaceutical Statistics最新文献_第10页

Statistical analysis plan considerations for autologous cell and cell-based gene therapy clinical trials. 自体细胞和基于细胞的基因治疗临床试验的统计分析计划考虑。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-07-16 DOI: 10.1080/10543406.2026.2685280

Patricia Anderson, Revathi Ananthakrishnan, Zhenzhen Xu, Khadija Rerhou Rantell, Susan Bell, Daniel Li, Allen Xue, Alan Y Chiang

引用次数: 0

Nonparametric testing methods based on relative effect in non-inferiority clinical trial with multiple experimental drugs. 多药非劣效性临床试验中基于相对效应的非参数检验方法。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-07-14 DOI: 10.1080/10543406.2026.2699856

Jieun Park, Jae Won Lee

引用次数: 0

Overview of dose-finding designs and trials in cell and gene therapies. 细胞和基因治疗的剂量发现设计和试验概述。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-07-13 DOI: 10.1080/10543406.2026.2684622

Revathi Ananthakrishnan, Bambang Adiwijaya, Alan Y Chiang, Cong Han, Qiuling Ally He, Andrew Lewandowski, Rong Liu, Frank Shen, Hao Sun, Daniel Li

引用次数: 0

Making every component count: Using the Shapley value to improve win ratio analysis. 使每一个组成部分计数：使用沙普利值提高胜率分析。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-07-12 DOI: 10.1080/10543406.2026.2699855

Valerie Fu

{"title":"Making every component count: Using the Shapley value to improve win ratio analysis.","authors":"Valerie Fu","doi":"10.1080/10543406.2026.2699855","DOIUrl":"https://doi.org/10.1080/10543406.2026.2699855","url":null,"abstract":"Clinical trials often use composite endpoints to combine several clinically relevant outcomes into one treatment comparison. The win ratio is attractive for such endpoints because it compares patients hierarchically according to clinical priority, but the resulting overall ratio does not show how the individual components contribute to the reported treatment effect. Reporting all partial win ratios is informative but can be difficult to interpret because these partial summaries are not an additive decomposition of the overall win ratio. We propose SEWRA (Shapley-Enhanced Win Ratio Analysis), which applies the Shapley value to the log win ratio computed over subsets of components. On this scale, component contributions sum exactly to the overall log win ratio and can also be reported as multiplicative factors on the original win ratio scale. We position SEWRA as an attribution tool that complements, rather than replaces, the overall win ratio and component-specific clinical summaries. Simulations and a recurrent-event case study illustrate how SEWRA separates hierarchy-induced component contributions that are not apparent from partial win ratios alone.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-10"},"PeriodicalIF":1.3,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148431577","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

Leveraging external controls in clinical trials: estimands, estimation, assumptions. 在临床试验中利用外部控制：估计、估计、假设。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-06-23 DOI: 10.1080/10543406.2026.2685275

Bo Liu, Fan Li, Rury R Holman, Laine E Thomas

{"title":"Leveraging external controls in clinical trials: estimands, estimation, assumptions.","authors":"Bo Liu, Fan Li, Rury R Holman, Laine E Thomas","doi":"10.1080/10543406.2026.2685275","DOIUrl":"10.1080/10543406.2026.2685275","url":null,"abstract":"It is increasingly common to augment randomized controlled trial with external controls from observational data, to evaluate the treatment effect of an intervention. Traditional approaches to treatment effect estimation involve ambiguous estimands and unrealistic or strong assumptions, such as mean exchangeability. We introduce a double-indexed notation for potential outcomes to define causal estimands transparently and clarify distinct sources of implicit bias. We show that the concurrent control arm is critical in assessing the plausibility of assumptions and providing unbiased causal estimation. We derive a consistent and locally efficient estimator for a class of weighted average treatment effect estimands that combines concurrent and external data without assuming mean exchangeability. This estimator incorporates an estimate of the systematic difference in outcomes between the concurrent and external units, of which we propose a Frisch-Waugh-Lovell style partial regression method to obtain. We compare the proposed methods with existing methods using extensive simulation and applied to cardiovascular clinical trials.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.3,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148304027","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 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-06-23 DOI: 10.1080/10543406.2026.2692181

引用次数: 0

Comparison of flexible parametric modeling and nonparametric methods to estimate restricted mean survival time: A simulation study. 估计受限平均生存时间的柔性参数建模与非参数方法的比较：仿真研究。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-06-21 DOI: 10.1080/10543406.2026.2663456

Ryusei Kimura, Shogo Nomura, Takahiro Hasegawa, Kohei Uemura

{"title":"Comparison of flexible parametric modeling and nonparametric methods to estimate restricted mean survival time: A simulation study.","authors":"Ryusei Kimura, Shogo Nomura, Takahiro Hasegawa, Kohei Uemura","doi":"10.1080/10543406.2026.2663456","DOIUrl":"https://doi.org/10.1080/10543406.2026.2663456","url":null,"abstract":"In randomized controlled trials with survival time as the primary endpoint, it can be difficult to evaluate treatment effects using hazard ratios, particularly when the proportional hazards (PH) assumption is violated. The difference in restricted mean survival time (RMST) up to a pre-specified time point, <math><mi>τ</mi></math>, is an alternative to the hazard ratio. However, the relative advantages of parametric (flexible parametric modeling [FPM]) and nonparametric approaches (direct integration of the Kaplan-Meier curve [DI], pseudo-observation [PO], and inverse probability of censoring weighting [IPCW]) for the estimation of RMST and its difference between groups are not clearly established. In this study, we performed comparative simulation studies to evaluate the performance of these methods for unadjusted and adjusted analyses in both PH and non-PH scenarios. For PO, IPCW, and FPM, we also considered a scenario where important covariates were adjusted via regression models. We obtained several key findings. For scenarios where the total number of events was <math><mo>></mo><mn>35</mn></math>, FPM tended to be unbiased, with higher power in PH scenarios. In non-PH scenarios, FPM exhibited slight bias with comparable power. For scenarios where the total number of events was <math><mo>≤</mo><mn>35</mn></math>, FPM showed unstable results. Among nonparametric methods, the bias and differences in power were negligible, except when the number of patients at risk at <math><mi>τ</mi></math> was <math><mo>≤</mo><mn>15</mn></math>; in this setting, IPCW showed conservative power and, under regression adjustment, a slight bias. These results provide a basis for the selection of methods to estimate RMST based on the expected prognosis.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-21"},"PeriodicalIF":1.3,"publicationDate":"2026-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148297427","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

Simulated treatment comparisons with jackknife pseudo values for estimating population-adjusted marginal treatment effects. 模拟处理比较与小刀伪值估计人口调整后的边际处理效果。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-06-19 DOI: 10.1080/10543406.2026.2685277

Sean Yiu, Kirsty Rhodes

{"title":"Simulated treatment comparisons with jackknife pseudo values for estimating population-adjusted marginal treatment effects.","authors":"Sean Yiu, Kirsty Rhodes","doi":"10.1080/10543406.2026.2685277","DOIUrl":"https://doi.org/10.1080/10543406.2026.2685277","url":null,"abstract":"Matching-adjusted indirect comparisons (MAIC) and simulated treatment comparisons (STC) are commonly used for indirect treatment comparisons when patient-level data are available for some treatments but not others. However, MAIC can become inefficient when covariate overlap across trials is limited, and STC may fail to estimate marginal (population average) treatment effects required for health technology assessment when non-linear outcome models, such as logistic or survival models, are used. We propose a new STC approach to address these limitations. For settings where a linear model for the treatment effect can be assumed (e.g. log odds ratios or log hazard ratios), we propose to apply STC with jackknife pseudo values of the marginal treatment effect estimate as the dependent variable, rather than the observed outcomes. These pseudo values decompose the marginal treatment effect estimate into individual patient contributions, which can then be regressed on patient-level characteristics to predict marginal treatment effects in alternative target populations. We illustrate the approach using two worked examples involving non-linear outcome models. In simulated trials of continuous, binary, and time-to-event outcomes, the proposed approach showed less bias than traditional STC for non-linear outcome models, and had lower mean squared error than MAIC, both under correct and incorrect specification of baseline covariate functional forms.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-22"},"PeriodicalIF":1.3,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148280096","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

Handling missing data using multiple imputation in hybrid control clinical trials with modified power prior. 修正功率先验的混合对照临床试验中使用多重输入处理缺失数据。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-06-11 DOI: 10.1080/10543406.2026.2676020

Sunao Shimada, Masataka Taguri

{"title":"Handling missing data using multiple imputation in hybrid control clinical trials with modified power prior.","authors":"Sunao Shimada, Masataka Taguri","doi":"10.1080/10543406.2026.2676020","DOIUrl":"10.1080/10543406.2026.2676020","url":null,"abstract":"Randomized Controlled Trials (RCTs) are the gold standard in clinical trials. If Historical Data (HD) on the standard of care is available, hybrid control clinical trials can provide more evidence than a standalone RCT with unequal allocation. HD for the control group can often be derived from real-world data, which frequently includes missing covariates data. However, such missingness may introduce bias depending on missing data mechanisms and analytical methods. In this study, we propose addressing covariate missingness under the missing at random assumption by multiple imputation. In the analysis stage, we utilize a combination of propensity score matching and modified power prior. The simulation showed that complete case analysis caused bias under outcome and covariate-dependent covariate missingness, while multiple imputation provided nearly unbiased estimates and improved precision when HD was similar to the current trial data under the missing at random assumption. HD was dynamically borrowed based on outcome similarity: improved estimation accuracy with reduced bias and increased power when outcomes were similar, while reasonably controlling the type I error when outcomes were dissimilar. The proposed method was also applied to a real clinical trial data to illustrate its practical performance. These results suggest that the approach may be useful in hybrid control clinical trials that utilize real world data with missing covariates.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-24"},"PeriodicalIF":1.3,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148220806","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 doubly robust estimator for unanchored indirect treatment comparisons: Development and evaluation using simulated and case-study data. 非锚定间接治疗比较的双重稳健估计：使用模拟和案例研究数据的开发和评估。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-06-11 DOI: 10.1080/10543406.2026.2676076

Yunhong Wu, Yu Yin, Yun Ling

引用次数: 0