Journal of Biopharmaceutical Statistics最新文献_第3页

Optimum designs for clinical trials in personalized medicine when response variance depends on treatment. 当反应差异取决于治疗方法时，个性化医疗临床试验的最佳设计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-31 DOI: 10.1080/10543406.2024.2395548

Belmiro P M Duarte, Anthony C Atkinson

{"title":"Optimum designs for clinical trials in personalized medicine when response variance depends on treatment.","authors":"Belmiro P M Duarte, Anthony C Atkinson","doi":"10.1080/10543406.2024.2395548","DOIUrl":"https://doi.org/10.1080/10543406.2024.2395548","url":null,"abstract":"We study optimal designs for clinical trials when the value of the response and its variance depend on treatment and covariates are included in the response model. Such designs are generalizations of Neyman allocation, commonly used in personalized medicine when external factors may have differing effects on the response depending on subgroups of patients. We develop theoretical results for D-, A-, E- and D<math><msub><mi> </mi><mrow><mrow><mi>A</mi></mrow></mrow></msub></math>-optimal designs and construct semidefinite programming (SDP) formulations that support their numerical computation. D-, A-, and E-optimal designs are appropriate for efficient estimation of distinct properties of the parameters of the response models. Our formulation allows finding optimal allocation schemes for a general number of treatments and of covariates. Finally, we study frequentist sequential clinical trial allocation within contexts where response parameters and their respective variances remain unknown. We illustrate, with a simulated example and with a redesigned clinical trial on the treatment of neuro-degenerative disease, that both theoretical and SDP results, derived under the assumption of known variances, converge asymptotically to allocations obtained through the sequential scheme. Procedures to use static and sequential allocation are proposed.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-18"},"PeriodicalIF":1.2,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114910","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

MOVER tests for non-inferiority of the difference between two binary-outcome treatments in the matched-pairs design. MOVER 检验配对设计中两种二元结果治疗之间的差异是否具有非劣效性。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-29 DOI: 10.1080/10543406.2024.2390888

Liangchang Xiu, Linlin Xie, Haiyi Yan, Chunxin Wu, Huansheng Liu, Chao Chen

{"title":"MOVER tests for non-inferiority of the difference between two binary-outcome treatments in the matched-pairs design.","authors":"Liangchang Xiu, Linlin Xie, Haiyi Yan, Chunxin Wu, Huansheng Liu, Chao Chen","doi":"10.1080/10543406.2024.2390888","DOIUrl":"https://doi.org/10.1080/10543406.2024.2390888","url":null,"abstract":"A non-inferiority trial is usually conducted to investigate whether a new drug/treatment is no worse than a reference drug/treatment by a small, pre-specified, non-inferiority margin. This study aimed to assess the non-inferiority of the difference between two binary-outcome treatments in a matched-pairs design based on the method of variance of estimates recovery (MOVER). The processes for estimating the confidence interval of a single proportion included in the MOVER are the Wilson score interval, Agresti - Coull interval, Jeffreys interval, modified Jeffreys interval, score method with continuity correction, and arcsin interval. The performance of the six MOVER tests, the fiducial test, and the restricted maximum likelihood estimation test were evaluated by comparing their type I error rates and power at different pre-assigned levels and with varying combinations of parameters. The evaluation results showed that the modified Jeffreys MOVER test can be a competitive alternative to the other recommended tests. It can control type I errors well, and its power is not inferior to other methods. The proposed tests were illustrated with three real-world examples.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-14"},"PeriodicalIF":1.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114909","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

Strategies for successful dose optimization in oncology drug development: a practical guide. 肿瘤药物研发中成功优化剂量的策略：实用指南。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-11 DOI: 10.1080/10543406.2024.2387364

Qiqi Deng, Lili Zhu, Brendan Weiss, Praveen Aanur, Lei Gao

{"title":"Strategies for successful dose optimization in oncology drug development: a practical guide.","authors":"Qiqi Deng, Lili Zhu, Brendan Weiss, Praveen Aanur, Lei Gao","doi":"10.1080/10543406.2024.2387364","DOIUrl":"https://doi.org/10.1080/10543406.2024.2387364","url":null,"abstract":"Dose optimization is a critical challenge in drug development. Historically, dose determination in oncology has followed a divergent path from other non-oncology therapeutic areas due to the unique characteristics and requirements in Oncology. However, with the emergence of new drug modalities and mechanisms of drugs in oncology, such as immune therapies, radiopharmaceuticals, targeted therapies, cytostatic agents, and others, the dose-response relationship for efficacy and toxicity could be vastly varied compared to the cytotoxic chemotherapies. The doses below the MTD may demonstrate similar efficacy to the MTD with an improved tolerability profile, resembling what is commonly observed in non-oncology treatments. Hence, alternate strategies for dose optimization are required for new modalities in oncology drug development. This paper delves into the historical evolution of dose finding methods from non-oncology to oncology, highlighting examples and summarizing the underlying drivers of change. Subsequently, a practical framework and guidance are provided to illustrate how dose optimization can be incorporated into various stages of the development program. We provide the following general recommendations: 1) The objective for phase I is to identify a dose range rather than a single MTD dose for subsequent development to better characterize the safety and tolerability profile within the dose range. 2) At least two doses separable by PK are recommended for dose optimization in phase II. 3) Ideally, dose optimization should be performed before launching the confirmatory study. Nevertheless, innovative designs such as seamless II/III design can be implemented for dose selection and may accelerate the drug development program.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-15"},"PeriodicalIF":1.2,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141914624","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

Simulating survival data when one subgroup lacks information. 在一个分组缺乏信息的情况下模拟生存数据。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-01 Epub Date: 2023-07-26 DOI: 10.1080/10543406.2023.2236218

Yiqi Zhao, Ping Yan, Xinfeng Yang

{"title":"Simulating survival data when one subgroup lacks information.","authors":"Yiqi Zhao, Ping Yan, Xinfeng Yang","doi":"10.1080/10543406.2023.2236218","DOIUrl":"10.1080/10543406.2023.2236218","url":null,"abstract":"In this paper, we aim to show the process of simulating survival data when the distribution of the overall population and one subgroup (called \"positive subgroup\") as well as the proportion of the subgroup is known, while the distribution of the other subgroup (called \"negative subgroup\") is unknown. We propose a combination method which generates survival data of the positive subgroup and negative subgroup, respectively, and survival data of the overall population are the combination of the two subgroups. The parameters of the overall population and the positive subgroup need to satisfy certain constraints, otherwise the parameters may lead to contradictions. From simulation, we show that our proposed combination method can reflect the correlation between the test statistics of overall population and positive subgroup, which makes the simulated data more realistic and the results of simulation more reliable. Moreover, for a multiplicity control in trial design, the combination method can help to determine the <math><mrow><mrow><mi>α</mi></mrow></mrow></math> splitting strategy between primary endpoints, and is helpful in designs of clinical trials as shown in three applications.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"613-625"},"PeriodicalIF":1.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9873752","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 hierarchical model for dose-finding trial incorporating historical data. 结合历史数据的剂量发现试验的贝叶斯层次模型。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-01 Epub Date: 2023-09-07 DOI: 10.1080/10543406.2023.2251578

Linxi Han, Qiqi Deng, Zhangyi He, Frank Fleischer, Feng Yu

引用次数: 0

The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study. 错误分类错误对基于下一代测序的生物标志物性能的影响，一项模拟研究。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-01 Epub Date: 2023-10-11 DOI: 10.1080/10543406.2023.2269251

Dong Wang, Sue-Jane Wang, Samir Lababidi

{"title":"The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study.","authors":"Dong Wang, Sue-Jane Wang, Samir Lababidi","doi":"10.1080/10543406.2023.2269251","DOIUrl":"10.1080/10543406.2023.2269251","url":null,"abstract":"The development of next-generation sequencing (NGS) opens opportunities for new applications such as liquid biopsy, in which tumor mutation genotypes can be determined by sequencing circulating tumor DNA after blood draws. However, with highly diluted samples like those obtained with liquid biopsy, NGS invariably introduces a certain level of misclassification, even with improved technology. Recently, there has been a high demand to use mutation genotypes as biomarkers for predicting prognosis and treatment selection. Many methods have also been proposed to build classifiers based on multiple loci with machine learning algorithms as biomarkers. How the higher misclassification rate introduced by liquid biopsy will affect the performance of these biomarkers has not been thoroughly investigated. In this paper, we report the results from a simulation study focused on the clinical utility of biomarkers when misclassification is present due to the current technological limit of NGS in the liquid biopsy setting. The simulation covers a range of performance profiles for current NGS platforms with different machine learning algorithms and uses actual patient genotypes. Our results show that, at the high end of the performance spectrum, the misclassification introduced by NGS had very little effect on the clinical utility of the biomarker. However, in more challenging applications with lower accuracy, misclassification could have a notable effect on clinical utility. The pattern of this effect can be complex, especially for machine learning-based classifiers. Our results show that simulation can be an effective tool for assessing different scenarios of misclassification.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"700-718"},"PeriodicalIF":1.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220408","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

Adaptive platform trials: the impact of common controls on type one error and power. 自适应平台试验:常用控制对第一类误差和功率的影响。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-01 Epub Date: 2023-11-21 DOI: 10.1080/10543406.2023.2275765

Quynh Nguyen, Katharina Hees, Benjamin Hofner

{"title":"Adaptive platform trials: the impact of common controls on type one error and power.","authors":"Quynh Nguyen, Katharina Hees, Benjamin Hofner","doi":"10.1080/10543406.2023.2275765","DOIUrl":"10.1080/10543406.2023.2275765","url":null,"abstract":"Platform trials offer a framework to study multiple interventions in one trial with the opportunity of opening and closing arms. The use of common controls can increase efficiency as compared to individual controls. The need for multiplicity adjustment because of common controls is currently a debate among researchers, pharmaceutical companies, and regulators. The impact of common controls on the type one error in a fixed platform trial, i.e. when all treatments start and end recruitment at the same time, has been discussed in the literature before. We complement these findings by investigating the impact of a common control on the type one error and power in a flexible platform trial, i.e. when one arm joins the platform later. We derived the correlation of test statistics to assess the impact of the overlap and compared the results to a trial with individual controls. Furthermore, we evaluate the power, and the impact of multiplicity adjustment on the power in fixed and flexible platform trials. These methodological considerations are complemented by a regulatory guideline review. With multiple arms, the FWER is inflated when no multiplicity adjustment is applied. However, the FWER inflation is smaller with common controls than with individual controls. Even after multiplicity adjustment, a trial with common controls is often beneficial in terms of sample size and power. However, in some cases, the trial with common controls loses the efficiency gain and it might be advisable to run a separate trial rather than joining a platform trial.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"719-736"},"PeriodicalIF":1.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138292447","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

Determining the late effect parameter in the Fleming-Harrington test using asymptotic relative efficiency in cancer immunotherapy clinical trials. 利用癌症免疫疗法临床试验中的渐近相对效率确定弗莱明-哈灵顿试验中的后期效应参数。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-01 Epub Date: 2023-08-10 DOI: 10.1080/10543406.2023.2244055

Yuichiro Kaneko, Satoshi Morita

{"title":"Determining the late effect parameter in the Fleming-Harrington test using asymptotic relative efficiency in cancer immunotherapy clinical trials.","authors":"Yuichiro Kaneko, Satoshi Morita","doi":"10.1080/10543406.2023.2244055","DOIUrl":"10.1080/10543406.2023.2244055","url":null,"abstract":"The delayed treatment effect, which manifests as a separation of survival curves after a change point, has often been observed in immunotherapy clinical trials. A late effect of this kind may violate the proportional hazards assumption, resulting in the non-negligible loss of statistical power of an ordinary log-rank test when comparing survival curves. The Fleming-Harrington (FH) test, a weighted log-rank test, is configured to mitigate the loss of power by incorporating a weight function with two parameters, one each for early and late treatment effects. The two parameters need to be appropriately determined, but no helpful guides have been fully established. Since the late effect is expected in immunotherapy trials, we focus on the late effect parameter in this study. We consider parameterizing the late effect in a readily interpretable fashion and determining the optimal late effect parameter in the FH test to maintain statistical power in reference to the asymptotic relative efficiency (ARE). The optimization is carried out under three lag models (i.e. linear, threshold, and generalized linear lag), where the optimal weights are proportional to the lag functions characterized by the change points. Extensive simulation studies showed that the FH test with the selected late parameter reliably provided sufficient power even when the change points in the lag models were misspecified. This finding suggests that the FH test with the ARE-guided late parameter may be a reasonable and practical choice for the primary analysis in immunotherapy clinical trials.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"626-645"},"PeriodicalIF":1.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10014033","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 variance estimation of target population created by inverse probability weighting. 基于逆概率加权的目标种群方差估计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-01 Epub Date: 2023-08-24 DOI: 10.1080/10543406.2023.2244593

Jinmei Chen, Rui Chen, Yuhao Feng, Ming Tan, Pingyan Chen, Ying Wu

{"title":"On variance estimation of target population created by inverse probability weighting.","authors":"Jinmei Chen, Rui Chen, Yuhao Feng, Ming Tan, Pingyan Chen, Ying Wu","doi":"10.1080/10543406.2023.2244593","DOIUrl":"10.1080/10543406.2023.2244593","url":null,"abstract":"Inverse probability weighting (IPW) is frequently used to reduce or minimize the observed confounding in observational studies. IPW creates a pseudo-sample by weighting each individual by the inverse of the conditional probability of receiving the treatment level that he/she has actually received. In the pseudo-sample there is no variation among the multiple individuals generated by weighting the same individual in the original sample. This would reduce the variability of the data and therefore bias the variance estimate in the target population. Conventional variance estimation methods for IPW estimators generally ignore this underestimation and tend to produce biased estimates of variance. We here propose a more reasonable method that incorporates this source of variability by using parametric bootstrapping based on intra-stratum variability estimates. This approach firstly uses propensity score stratification and intra-stratum standard deviation to approximate the variability among multiple individuals generated based on a single individual whose propensity score falls within the corresponding stratum. The parametric bootstrapping is then used to incorporate the target variability by re-generating outcomes after adding a random error term to the original data. The performance of the proposed method is compared with three existing methods including the naïve model-based variance estimator, the nonparametric bootstrap variance estimator, and the robust variance estimator in the simulation section. An example of patients with sarcopenia is used to illustrate the implementation of the proposed approach. According to the results, the proposed approach has desirable statistical properties and can be easily implemented using the provided R code.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"661-679"},"PeriodicalIF":1.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10314461","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

Estimation of treatment effects in early-phase randomized clinical trials involving external control data. 涉及外部对照数据的早期随机临床试验中治疗效果的评估。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-08-01 Epub Date: 2023-10-12 DOI: 10.1080/10543406.2023.2256835

Heiko Götte, Marietta Kirchner, Johannes Krisam, Arthur Allignol, Armin Schüler, Meinhard Kieser

{"title":"Estimation of treatment effects in early-phase randomized clinical trials involving external control data.","authors":"Heiko Götte, Marietta Kirchner, Johannes Krisam, Arthur Allignol, Armin Schüler, Meinhard Kieser","doi":"10.1080/10543406.2023.2256835","DOIUrl":"10.1080/10543406.2023.2256835","url":null,"abstract":"There are good reasons to perform a randomized controlled trial (RCT) even in early phases of clinical development. However, the low sample sizes in those settings lead to high variability of the treatment effect estimate. The variability could be reduced by adding external control data if available. For the common setting of suitable subject-level control group data only available from one external (clinical trial or real-world) data source, we evaluate different analysis options for estimating the treatment effect via hazard ratios. The impact of the external control data is usually guided by the level of similarity with the current RCT data. Such level of similarity can be determined via outcome and/or baseline covariate data comparisons. We provide an overview over existing methods, propose a novel option for a combined assessment of outcome and baseline data, and compare a selected set of approaches in a simulation study under varying assumptions regarding observable and unobservable confounder distributions using a time-to-event model. Our various simulation scenarios also reflect the differences between external clinical trial and real-world data. Data combinations via simple outcome-based borrowing or simple propensity score weighting with baseline covariate data are not recommended. Analysis options which conflate outcome and baseline covariate data perform best in our simulation study.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"680-699"},"PeriodicalIF":1.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220407","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