Journal of Biopharmaceutical Statistics最新文献

Improving power in adaptive expansion of biomarker populations in phase 3 clinical trials.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-19 DOI: 10.1080/10543406.2025.2469871

Guoqing Diao, Xun Jiang, Donglin Zeng, May Mo, H Amy Xia, Joseph G Ibrahim

{"title":"Improving power in adaptive expansion of biomarker populations in phase 3 clinical trials.","authors":"Guoqing Diao, Xun Jiang, Donglin Zeng, May Mo, H Amy Xia, Joseph G Ibrahim","doi":"10.1080/10543406.2025.2469871","DOIUrl":"https://doi.org/10.1080/10543406.2025.2469871","url":null,"abstract":"With the availability of unprecedented human genomic biomarker data, incorporating such biomarker data has received a lot of attention in phase 3 clinical trials. One particular enrichment design proposed recently in the literature is to recruit more biomarker positive patients in an all-comer study if the treatment effect in the biomarker negative group is less promising than expected. The intuition is to improve the chance of success of the trial since the success rate in the all-comer population may be low. We propose an enrichment design that unifies the existing biomarker adaptive designs for phase 3 clinical trials. In addition, we propose a new test accounting for the correlations among the test statistics based on different groups of patients, including all-comers, biomarker positive patients only, and biomarker negative patients only. We investigate the theoretical properties of the design and demonstrate the new test accurately controls the type I error rate and gains power over existing methods through extensive simulations. A computer program is developed for power calculations given a set of design parameters, including the proportion of biomarker positive patients, the distribution of the failure time in each treatment and biomarker group, and the number of patients in the first stage and the second stage (i.e. the enrichment stage), among others.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-12"},"PeriodicalIF":1.2,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665396","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

Efficient estimation of the cox model when incorporating the subgroup restricted mean survival time.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-13 DOI: 10.1080/10543406.2024.2444242

Jo-Ying Hung, Junjiang Zhong, Huang-Tz Ou, Pei-Fang Su

引用次数: 0

Statistical considerations for using tolerance interval to set product specification for normally distributed attribute.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-13 DOI: 10.1080/10543406.2025.2473612

Chang Chen, Yi Tsong, Xutong Zhao, Meiyu Shen

{"title":"Statistical considerations for using tolerance interval to set product specification for normally distributed attribute.","authors":"Chang Chen, Yi Tsong, Xutong Zhao, Meiyu Shen","doi":"10.1080/10543406.2025.2473612","DOIUrl":"https://doi.org/10.1080/10543406.2025.2473612","url":null,"abstract":"Conventionally, the product quality specification and control chart limits are determined as the mean plus and minus 3 sample standard deviations with the assumption that the quality data is normally distributed. These limits correspond to an interval centered at the mean, covering approximately 97.3% of the population. The estimate of such an interval is called the <math><mi>β</mi></math>-content tolerance interval. It has been proposed to use a two one-sided <math><mi>β</mi></math>-content tolerance interval approach for determining drug product quality specifications. For a given confidence level, <math><mn>1</mn><mo>-</mo><mi>α</mi><mo>,</mo></math> and a coverage percentage p, the <math><mi>β</mi></math>-content tolerance interval is not precise when the sample size is small. For the derivation of a precise <math><mi>β</mi></math>-content tolerance interval, Faulkenberry and Daly proposed a \"goodness\" criterion for sample size determination. In order to avoid overestimating the <math><mi>β</mi></math>-content tolerance interval when p is large, we propose to define the precision requirement as the probability of the tolerance interval covering more than <math><mrow><mfrac><mrow><mfenced><mrow><mn>1</mn><mo>+</mo><mi>p</mi></mrow></mfenced></mrow><mn>2</mn></mfrac></mrow></math> is restricted to a pre-specified significance level <math><msup><mi>α</mi><mo>'</mo></msup></math>. Quality specification studies are often not planned with proper sample sizes. To obtain precise <math><mi>β</mi></math>-content tolerance intervals for quality specification studies, the proper coverage p satisfying the \"goodness\" criterion and the minimum sample sizes were also determined with the pre-specified significance level <math><msup><mi>α</mi><mo>'</mo></msup></math>. With this approach, one may properly set the product specificationwhile avoiding over-specifying the quality limits.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-6"},"PeriodicalIF":1.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617676","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

Recurrent neural networks and attention scores for personalized prediction and interpretation of patient-reported outcomes.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-13 DOI: 10.1080/10543406.2025.2469884

Jinxiang Hu, Mohsen Nayebi Kerdabadi, Xiaohang Mei, Joseph Cappelleri, Richard Barohn, Zijun Yao

{"title":"Recurrent neural networks and attention scores for personalized prediction and interpretation of patient-reported outcomes.","authors":"Jinxiang Hu, Mohsen Nayebi Kerdabadi, Xiaohang Mei, Joseph Cappelleri, Richard Barohn, Zijun Yao","doi":"10.1080/10543406.2025.2469884","DOIUrl":"https://doi.org/10.1080/10543406.2025.2469884","url":null,"abstract":"We proposed an Interpretable Personalized Artificial Intelligence (AI) model for PRO measures via Recurrent Neural Networks (RNN) and attention scores, with data from an open label randomized clinical trial of pain in 402 participants with cryptogenic sensory polyneuropathy at 40 neurology care clinics. All patients were assigned to four treatment groups: nortriptyline, duloxetine, pregabalin, and mexiletine. Each patient had 4 PRO measures (quality of life SF-12; PROMIS: pain interference, fatigue, sleep disturbance) at 4 time points (baseline, week 4, week 8, and week 12). We included 201 patients without missing values. Participants were 30 years or older and 106 (52.7%) were men, majority were White (164, 81.6%). We fitted an RNN model with attention scores to the data to predict the PROMIS pain interference score. We evaluated the model performance with Mean Absolute Error (MAE) and R-square statistics. We also used attention scores to explain the global variable importance at model level, and at individual level for each patient. The best predictor of pain score was the SF-12 item (physical and emotional health interfere with social activities) and fatigue item (push yourself to get things done), the biggest drug-level contributor was mexiletine, the biggest time-level contributor was week 12. Overall, the model fit well (MAE = 3.7, R2 = 63%). Attention-RNN is a feasible and reliable model for predicting PRO outcomes utilizing longitudinal data, such as pain, and can provide personalized individual level interpretation.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-11"},"PeriodicalIF":1.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626027","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 Bayesian framework for safety signal detection from medical device data.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-10 DOI: 10.1080/10543406.2025.2464595

Jianjin Xu, Adrijo Chakraborty, Archie Sachdeva, Ram Tiwari

{"title":"A Bayesian framework for safety signal detection from medical device data.","authors":"Jianjin Xu, Adrijo Chakraborty, Archie Sachdeva, Ram Tiwari","doi":"10.1080/10543406.2025.2464595","DOIUrl":"https://doi.org/10.1080/10543406.2025.2464595","url":null,"abstract":"Safety evaluation is important during both the pre-market clinical trials and post-market surveillance. In either a pre-market or post-market setting wherein the safety of a device is compared to that of a control device, it is desirable to identify any difference in the safety between two devices as expeditiously as possible. Here, we introduce the Bayesian hierarchical framework for the safety assessment in two-arm clinical trials, with signal detection accomplished by evaluating the effect size of each adverse event (AE) measured by odds ratio or relative risk. The framework starts with a standard hierarchical Bayesian model with a parametric distribution as a common prior for the effect sizes of all AEs. Then, it is extended with a non-parametric prior, Dirichlet Process Prior, to allow for more flexibility. After that, to account for the rare events in some trials, it is further extended with the option of additional zero-inflated parameters and calculation of regularized effect size. Extra incorporation of exposure-time information is available under each framework. The performance of the proposed technique, along with its extensions, is studied by simulation. The application of the proposed Bayesian framework is demonstrated by data from a two-device clinical trial, the newer left ventricular assist device (LVAD) and the existing LVAD. The Bayesian analysis result is then compared to a traditional frequentist technique. Through both simulation and application, the proposed Bayesian technique is shown to be robust to the selection of priors of the variance component, and has comparative and under some scenarios even better performance than the frequentist technique. Overall, the developed Bayesian framework is a feasible alternative to the frequentist method for safety evaluation of medical device clinical trials.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-17"},"PeriodicalIF":1.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586908","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 basket trial design based on constrained hierarchical Bayesian model for latent subgroups. 基于分层贝叶斯约束模型的潜伏亚组篮式试验设计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-02-18 DOI: 10.1080/10543406.2024.2311851

Kentaro Takeda, Atsuki Hashimoto, Shufang Liu, Alan Rong

{"title":"A basket trial design based on constrained hierarchical Bayesian model for latent subgroups.","authors":"Kentaro Takeda, Atsuki Hashimoto, Shufang Liu, Alan Rong","doi":"10.1080/10543406.2024.2311851","DOIUrl":"10.1080/10543406.2024.2311851","url":null,"abstract":"It is well known a basket trial consisting of multiple cancer types has the potential of borrowing strength across the baskets defined by the cancer types, leading to an efficient design in terms of sample size and trial duration. The treatment effects in those baskets are often heterogeneous and categorized by the cancer types being sensitive or insensitive to the treatment. Hence, the assumption of exchangeability in many existing basket trials may be violated, and there is a need to design trials without this assumption. In this paper, we simplify the constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) for two classifiers to deal with the potential heterogeneity of treatment effects due to the single classifier of the cancer type. Different baskets are aggregated into subgroups using a latent subgroup modeling approach. The treatment effects are similar and exchangeable to facilitate information borrowing within each latent subgroup. Applying the simplified CHBM-LS approach to the real basket trials where baskets defined by only cancer types shows better performance than other available approaches. Further simulation study also demonstrates this CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"271-282"},"PeriodicalIF":1.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139900903","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

Sequential monitoring of cancer immunotherapy trial with random delayed treatment effect. 对随机延迟治疗效果的癌症免疫疗法试验进行序列监测。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2023-12-25 DOI: 10.1080/10543406.2023.2296055

Jianrong Wu, Liang Zhu, Yimei Li

{"title":"Sequential monitoring of cancer immunotherapy trial with random delayed treatment effect.","authors":"Jianrong Wu, Liang Zhu, Yimei Li","doi":"10.1080/10543406.2023.2296055","DOIUrl":"10.1080/10543406.2023.2296055","url":null,"abstract":"Cancer immunotherapy trials are frequently characterized by a delayed treatment effect that violates the proportional hazards assumption. The log-rank test (LRT) suffers a substantial loss of statistical power under the nonproportional hazards model. Various group sequential designs using weighted LRTs (WLRTs) have been proposed under the fixed delayed treatment effect model. However, patients enrolled in immunotherapy trials are often heterogeneous, and the duration of the delayed treatment effect is a random variable. Therefore, we propose group sequential designs under the random delayed effect model using the random delayed distribution WLRT. The proposed group sequential designs are developed for monitoring the efficacy of the trial using the method of Lan-DeMets alpha-spending function with O'Brien-Fleming stopping boundaries or a gamma family alpha-spending function. The maximum sample size for the group sequential design is obtained by multiplying an inflation factor with the sample size for the fixed sample design. Simulations are conducted to study the operating characteristics of the proposed group sequential designs. The robustness of the proposed group sequential designs for misspecifying random delay time distribution and domain is studied via simulations.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"227-240"},"PeriodicalIF":1.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11196386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139038220","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

Interval estimation of relative risks for combined unilateral and bilateral correlated data. 合并单侧和双侧相关数据的相对风险区间估计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-01-09 DOI: 10.1080/10543406.2023.2297789

Kejia Wang, Chang-Xing Ma

{"title":"Interval estimation of relative risks for combined unilateral and bilateral correlated data.","authors":"Kejia Wang, Chang-Xing Ma","doi":"10.1080/10543406.2023.2297789","DOIUrl":"10.1080/10543406.2023.2297789","url":null,"abstract":"Measurements are generally collected as unilateral or bilateral data in clinical trials, epidemiology, or observational studies. For example, in ophthalmology studies, the primary outcome is often obtained from one eye or both eyes of an individual. In medical studies, the relative risk is usually the parameter of interest and is commonly used. In this article, we develop three confidence intervals for the relative risk for combined unilateral and bilateral correlated data under the equal dependence assumption. The proposed confidence intervals are based on maximum likelihood estimates of parameters derived using the Fisher scoring method. Simulation studies are conducted to evaluate the performance of proposed confidence intervals with respect to the empirical coverage probability, the mean interval width, and the ratio of mesial non-coverage probability to the distal non-coverage probability. We also compare the proposed methods with the confidence interval based on the method of variance estimates recovery and the confidence interval obtained from the modified Poisson regression model with correlated binary data. We recommend the score confidence interval for general applications because it best controls converge probabilities at the 95% level with reasonable mean interval width. We illustrate the methods with a real-world example.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"163-186"},"PeriodicalIF":1.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139405311","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

Miettinen and Nurminen score statistics revisited. 米特宁和努尔米宁分数统计再探。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-02-09 DOI: 10.1080/10543406.2024.2311242

Antonio Martín Andrés, Francisco Gayá Moreno, María Álvarez Hernández, Inmaculada Herranz Tejedor

{"title":"Miettinen and Nurminen score statistics revisited.","authors":"Antonio Martín Andrés, Francisco Gayá Moreno, María Álvarez Hernández, Inmaculada Herranz Tejedor","doi":"10.1080/10543406.2024.2311242","DOIUrl":"10.1080/10543406.2024.2311242","url":null,"abstract":"It is commonly necessary to perform inferences on the difference, ratio, and odds ratio of two proportions p1 and p2 based on two independent samples. For this purpose, the most common asymptotic statistics are based on the score statistics (S-type statistics). As these do not correct the bias of the estimator of the product pi (1-pi), Miettinen and Nurminen proposed the MN-type statistics, which consist of multiplying the statistics S by (N-1)/N, where N is the sum of the two sample sizes. This paper demonstrates that the factor (N-1)/N is only correct in the case of the test of equality of two proportions, providing the estimation of the correct factor (AU-type statistics) and the minimum value of the same (AUM-type statistics). Moreover, this paper assesses the performance of the four-type statistics mentioned (S, MN, AU and AUM) in one and two-tailed tests, and for each of the three parameters cited (d, R and OR). We found that the AUM-type statistics are the best, followed by the MN type (whose performance was most similar to that of AU-type). Finally, this paper also provides the correct factors when the data are from a multinomial distribution, with the novelty that the MN and AU statistics are similar in the case of the test for the odds ratio.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"283-296"},"PeriodicalIF":1.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139713444","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

Combination MCP-Mod for two-drug combination dose-ranging studies. 用于双药联合剂量范围研究的 MCP-Mod 组合。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-02-09 DOI: 10.1080/10543406.2024.2311254

Yifan Zhou, Abigail Sloan, Sandeep Menon, Ling Wang

{"title":"Combination MCP-Mod for two-drug combination dose-ranging studies.","authors":"Yifan Zhou, Abigail Sloan, Sandeep Menon, Ling Wang","doi":"10.1080/10543406.2024.2311254","DOIUrl":"10.1080/10543406.2024.2311254","url":null,"abstract":"Combination therapies with multiple mechanisms of action can offer improved efficacy and/or safety profiles when compared to a single therapy with one mechanism of action. Consequently, the number of combination therapy studies have increased multi-fold, both in oncology and non-oncology indications. However, identifying the optimal doses of each drug in a combination therapy can require a large sample size and prolong study timelines, especially when full factorial designs are used. In this paper, we extend the MCP-Mod design of Bretz, Pinheiro, and Branson to a three-dimensional space to model the dose-response surface of a two-drug combination under the framework of Combination (Comb) MCP-Mod. The resulting model yields a set of dosages for each drug in the combination that elicits the target response so that an optimal dose for the combination can be selected for pivotal studies. We construct three-dimensional dose-response models for the combination and formulate the contrast test statistic to select the best model, which can then be used to select the optimal dose. Guidance to calculate power and sample size calculations are provided to assist study design. Simulation studies show that Comb MCP-Mod performs as well as the conventional multiple comparisons approach in controlling the family-wise error rate at the desired alpha level. However, Comb MCP-Mod is more powerful than the classical multiple comparisons approach in detecting dose-response relationships when treatment is non-null. The probability of correctly identifying the underlying dose-response relationship is generally higher when using Comb MCP-Mod than when using the multiple comparisons approach.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"257-270"},"PeriodicalIF":1.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139713443","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