Journal of Biopharmaceutical Statistics最新文献_第4页

Multiple test procedures of disease prevalence based on stratified partially validated series in the presence of a gold standard. 在金标准存在的情况下，基于分层部分验证序列的疾病流行率的多重测试程序。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-01 Epub Date: 2023-10-19 DOI: 10.1080/10543406.2023.2269262

Shi-Fang Qiu, Xiao-Liang Zhang, Ying-Qiu Qu, Yuan-Quan Han

{"title":"Multiple test procedures of disease prevalence based on stratified partially validated series in the presence of a gold standard.","authors":"Shi-Fang Qiu, Xiao-Liang Zhang, Ying-Qiu Qu, Yuan-Quan Han","doi":"10.1080/10543406.2023.2269262","DOIUrl":"10.1080/10543406.2023.2269262","url":null,"abstract":"This paper discusses the problem of disease prevalence in clinical studies, focusing on multiple comparisons based on stratified partially validated series in the presence of a gold standard. Five test statistics, including two Wald-type test statistics, the inverse hyperbolic tangent transformation test statistic, likelihood ratio test statistic, and score test statistic, are proposed to conduct multiple comparisons. To control the overall type I error rate, several adjustment procedures are developed, namely the Bonferroni, Single-step adjusted MaxT, Single-step adjusted MinP, Holm's Step-down, and Hochberg's step-up procedures, based on these test statistics. The performance of the proposed methods is evaluated through simulation studies in terms of the empirical type I error rate and empirical power. Simulation results show that the Single-step adjusted MaxT procedure and Single-step adjusted MinP procedure generally outperform the other three procedures, and these two test procedures based on all test statistics have satisfactory performance. Notably, the Single-step adjusted MinP procedure tends to exhibit higher empirical power than the Single-step adjusted MaxT procedure. Furthermore, the Step-down and Step-up procedures show greater power compared to the Bonferroni method. The study also observes that as the validated ratio increases, the empirical type I errors of all test procedures approach the nominal level while maintaining higher power. Two real examples are presented to illustrate the proposed methods.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"753-774"},"PeriodicalIF":1.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49685240","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 systematic approach to adaptive sequential design for clinical trials: using simulations to select a design with desired operating characteristics. 临床试验适应性顺序设计的系统方法：利用模拟选择具有理想运行特征的设计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-01 Epub Date: 2024-05-30 DOI: 10.1080/10543406.2024.2358796

Ping Gao, Weidong Zhang

引用次数: 0

Up-front matching: an ongoing recruitment method for prospective observational studies that mimics randomization for selected baseline covariates. 前期匹配：前瞻性观察研究的一种持续招募方法，对选定的基线协变量进行模拟随机化。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-22 DOI: 10.1080/10543406.2024.2373436

William H Olson, Ibrahim Turkoz

{"title":"Up-front matching: an ongoing recruitment method for prospective observational studies that mimics randomization for selected baseline covariates.","authors":"William H Olson, Ibrahim Turkoz","doi":"10.1080/10543406.2024.2373436","DOIUrl":"https://doi.org/10.1080/10543406.2024.2373436","url":null,"abstract":"In a prospective observational study (POS) designed to assess the average causal effect of a treatment (e.g. Drug A) compared to a comparator (e.g. Drug B) in the treatment population, enrolling all patients who are assigned to the treatments of interest for follow-up has a potentially large negative impact on the statistical efficiency and bias of the analysis of the outcomes and on the cost of the study. \"Up-front matching\" is an innovative enrollment method for selecting patients for long-term follow-up among those who have already been assigned to treatment or comparator which uses frequency matching and hence avoids the restrictions of individual matching that other methods have used. To achieve potential statistical and logistical efficiencies in the POS, in up-front matching, a target population is defined based on a retrospective database which then enables selecting populations of patients for follow-up that have desirable statistical properties. In particular, the resulting populations of patients who are enrolled look like the population of treatment patients were randomized to treatment or comparator for the baseline covariates that are used to select patients for follow-up. The method is illustrated in detail for a study designed to assess the effect of injectable antipsychotics versus oral antipsychotics.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-14"},"PeriodicalIF":1.2,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749792","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 pharmacokinetic parameters as covariate in Bayesian logistic regression model to optimize dose selection in early phase oncology trial. 利用贝叶斯逻辑回归模型中的药代动力学参数作为协变量，优化早期肿瘤试验的剂量选择。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-19 DOI: 10.1080/10543406.2024.2379357

Xin Wei, Xiaosong Li, Ziyan Guo

{"title":"Leveraging pharmacokinetic parameters as covariate in Bayesian logistic regression model to optimize dose selection in early phase oncology trial.","authors":"Xin Wei, Xiaosong Li, Ziyan Guo","doi":"10.1080/10543406.2024.2379357","DOIUrl":"https://doi.org/10.1080/10543406.2024.2379357","url":null,"abstract":"Dose selection and optimization in early phase of oncology drug development serves as the foundation for the success of late phases drug development. Bivariate Bayesian logistic regression model (BLRM) is a widely utilized model-based algorithm that has been shown to improve the accuracy for identifying recommended phase 2 dose (RP2D) based on dose-limiting-toxicity (DLT) over traditional method such as 3 + 3. However, it remains a challenge to optimize dose selection that strikes a proper balance between safety and efficacy in escalation and expansion phase of phase I trials. In this paper, we first use a phase I clinical trial to demonstrate how the variability of drug exposure related to pharmacokinetic (PK) parameters among trial participants may add to the difficulties of identifying optimal dose. We use simulation to show that concurrently or retrospectively fitting BLRM model for dose/toxicity data from escalation phase with dose-independent PK parameters as covariate lead to improved accuracy of identifying dose level at which DLT rate is within a prespecified toxicity interval. Furthermore, we proposed both model- and rule-based methods to modify dose at patient level in expansion cohorts based on their PK/exposure parameters. Simulation studies show this approach leads to higher likelihood for a dose level with a manageable toxicity and desirable efficacy margin to be advanced to late phase pipeline after being screened at expansion phase of phase I trial.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-22"},"PeriodicalIF":1.2,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725123","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 score-goldilocks design for phase 3 clinical trials. 3 期临床试验的 "得分-金锁 "设计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-12 DOI: 10.1080/10543406.2024.2374850

Yingqiu Li, Xun Zhang, Zhimao Weng

引用次数: 0

A constrained optimum adaptive design for dose finding in early phase clinical trials. 用于早期临床试验剂量查找的受限最佳自适应设计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-10 DOI: 10.1080/10543406.2024.2373452

M Iftakhar Alam, Barbara Bogacka, D Stephen Coad

{"title":"A constrained optimum adaptive design for dose finding in early phase clinical trials.","authors":"M Iftakhar Alam, Barbara Bogacka, D Stephen Coad","doi":"10.1080/10543406.2024.2373452","DOIUrl":"10.1080/10543406.2024.2373452","url":null,"abstract":"Recently, interest has grown in the development of dose-finding methods that consider both toxicity and efficacy as endpoints. Along with responses on these, the incorporation of pharmacokinetic (PK) data can be beneficial in terms of patients' safety and can also increase the efficiency of the design for finding the best dose for the next phase. In this paper, the maximum concentration (<math><mrow><msub><mi>C</mi><mrow><mo>max</mo></mrow></msub></mrow></math>) is used as the PK measure guiding the dose selection. The ethically attractive approach, which is based on the probability of efficacy, is used as a dose optimisation criterion. At each stage of an adaptive trial, that dose is selected for which the criterion is maximised, subject to the constraints imposed on the <math><mrow><msub><mi>C</mi><mrow><mo>max</mo></mrow></msub></mrow></math> and the probability of toxicity. The inter-patient variability of the PK model parameters is considered, and population <math><mi>D</mi></math>-optimal sampling time points for measuring the concentration of a drug in the blood are calculated. The method is illustrated with a one-compartment PK model with first-order absorption, with the parameters being assumed to be random. The Cox model for bivariate binary responses is employed to model the dose-response outcomes. The results of a simulation study for several plausible dose-response scenarios show a significant gain in the efficiency of the design, as well as a reduction in the proportion of toxic responses.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-26"},"PeriodicalIF":1.2,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141565071","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

Response to comment on "Transporting survival of an HIV clinical trial to the external target populations by Lee et al. (2024)". 对 "Lee 等人将艾滋病临床试验的存活率转移到外部目标人群（2024 年）"评论的答复。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-08 DOI: 10.1080/10543406.2024.2373449

Shu Yang, Xiang Zhang

引用次数: 0

A Bayesian phase I-II clinical trial design to find the biological optimal dose on drug combination. 贝叶斯 I-II 期临床试验设计，寻找药物组合的生物最佳剂量。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-03 Epub Date: 2023-07-17 DOI: 10.1080/10543406.2023.2236208

Ziqing Wang, Jingyi Zhang, Tian Xia, Ruyue He, Fangrong Yan

引用次数: 0

Two-stage response adaptive randomization designs for multi-arm trials with binary outcome. 二元结果多臂试验的两阶段反应自适应随机化设计。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-03 Epub Date: 2023-07-15 DOI: 10.1080/10543406.2023.2234028

Xinlin Lu, Guogen Shan

{"title":"Two-stage response adaptive randomization designs for multi-arm trials with binary outcome.","authors":"Xinlin Lu, Guogen Shan","doi":"10.1080/10543406.2023.2234028","DOIUrl":"10.1080/10543406.2023.2234028","url":null,"abstract":"In recent years, adaptive randomization methods have gained significant popularity in clinical research and trial design due to their ability to provide both efficiency and flexibility in adjusting the statistical procedures of ongoing clinical trials. For a study to compare multiple treatments, a multi-arm two-stage design could be utilized to select the best treatment from the first stage and further compare that treatment with control in the second stage. The traditional design used equal randomization in both stages. To better utilize the interim results from the first stage, we propose to develop response adaptive randomization two-stage designs for a multi-arm clinical trial with binary outcome. Two allocation methods are considered: (1) an optimal allocation based on a sequential design; (2) the play-the-winner rule. Optimal multi-arm two-stage designs are obtained under three criteria: minimizing the expected number of failures, minimizing the average expected sample size, and minimizing the expected sample size under the null hypothesis. Simulation studies show that the proposed adaptive design based on the play-the-winner rule has good performance. A phase II trial for patients with pancreas adenocarcinoma and a germline BRCA<math><mrow><mo>/</mo></mrow></math>PALB2 mutation was used to illustrate the application of the proposed response adaptive randomization designs.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"526-538"},"PeriodicalIF":1.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10788381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9782235","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

Statistical Considerations and Software for Designing Sequential, Multiple Assignment, Randomized Trials (SMART) with a Survival Final Endpoint. 设计以生存为最终终点的连续、多次分配、随机试验 (SMART) 的统计考虑因素和软件。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-03 Epub Date: 2023-07-11 DOI: 10.1080/10543406.2023.2233616

Sasha Kravets, Amy S Ruppert, Sawyer B Jacobson, Jennifer G Le-Rademacher, Sumithra J Mandrekar

{"title":"Statistical Considerations and Software for Designing Sequential, Multiple Assignment, Randomized Trials (SMART) with a Survival Final Endpoint.","authors":"Sasha Kravets, Amy S Ruppert, Sawyer B Jacobson, Jennifer G Le-Rademacher, Sumithra J Mandrekar","doi":"10.1080/10543406.2023.2233616","DOIUrl":"10.1080/10543406.2023.2233616","url":null,"abstract":"Sequential, multiple assignment, randomized trial (SMART) designs are appropriate for comparing adaptive treatment interventions, in which intermediate outcomes (called tailoring variables) guide subsequent treatment decisions for individual patients. Within a SMART design, patients may be re-randomized to subsequent treatments following the outcomes of their intermediate assessments. In this paper, we provide an overview of statistical considerations necessary to design and implement a two-stage SMART design with a binary tailoring variable and a survival final endpoint. A chronic lymphocytic leukemia trial with a final endpoint of progression-free survival is used as an example for the simulations to assess how design parameters, including, choice of randomization ratios for each stage of randomization, and response rates of the tailoring variable affect the statistical power. We assess the choice of weights from restricted re-randomization on data analyses and appropriate hazard rate assumptions. Specifically, for a given first-stage therapy and prior to the tailoring variable assessment, we assume equal hazard rates for all patients randomized to a treatment arm. After the tailoring variable assessment, individual hazard rates are assumed for each intervention path. Simulation studies demonstrate that the response rate of the binary tailoring variable impacts power as it directly impacts the distribution of patients. We also confirm that when the first stage randomization is 1:1, it is not necessary to consider the first stage randomization ratio when applying the weights. We provide an R-shiny application for obtaining power for a given sample size for SMART designs.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"539-552"},"PeriodicalIF":1.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9826496","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