Journal of Biopharmaceutical Statistics最新文献

Latent class analysis of post-acute sequelae of SARS-CoV-2 infection. 对 SARS-CoV-2 感染后急性后遗症的潜伏类分析。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-11-16 DOI: 10.1080/10543406.2024.2424844

Xiaowu Sun, Jonathan P DeShazo, Laura Anatale-Tardiff, Manuela Di Fusco, Kristen E Allen, Thomas M Porter, Henriette Coetzer, Santiago M C Lopez, Laura Puzniak, Joseph C Cappelleri

{"title":"Latent class analysis of post-acute sequelae of SARS-CoV-2 infection.","authors":"Xiaowu Sun, Jonathan P DeShazo, Laura Anatale-Tardiff, Manuela Di Fusco, Kristen E Allen, Thomas M Porter, Henriette Coetzer, Santiago M C Lopez, Laura Puzniak, Joseph C Cappelleri","doi":"10.1080/10543406.2024.2424844","DOIUrl":"https://doi.org/10.1080/10543406.2024.2424844","url":null,"abstract":"Symptoms post-SARS-CoV-2 infection may persist for months and cause significant impairment and impact to quality of life. Acute symptoms of SARS-CoV-2 infection are well studied, yet data on clusters of symptoms over time, or post-acute sequelae of SARS-CoV-2 infection (PASC), are limited. We aim to characterize PASC phenotypes by identifying symptom clusters over a six-month period following infection in individuals vaccinated (boosted and not) and those unvaccinated. Subjects with ≥1 self-reported symptom and positive RT-PCR for SARS-CoV-2 at CVS Health US test sites were recruited between January and April 2022. Patient-reported outcomes symptoms, health-related quality of life (HRQoL), work productivity and activity impairment (WPAI) were captured at 1 month, 3 months, and 6 months post-acute infection. Phenotypes of PASC were determined based on subject matter knowledge and balanced consideration of statistical criteria (lower AIC, lower BIC, and adequate entropy) and interpretability. Generalized estimation equation approach was used to investigate relationship between QoL, WPAI and number of symptoms and identified phenotypes, and relationship between phenotypes and vaccination status as well. LCA identified three phenotypes that are primarily differentiated by number of symptoms. These three phenotypes remained consistent across time periods. Subjects with more symptoms were associated with lower HRQoL, and worse WPAI scores. Vaccinated individuals were more likely to be in the low symptom burden latent classes at all time points compared to unvaccinated individuals.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645140","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

Statistical operating characteristics of current early phase dose finding designs with toxicity and efficacy in oncology. 目前肿瘤学早期阶段剂量发现设计与毒性和疗效的统计运行特征。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-11-16 DOI: 10.1080/10543406.2024.2424845

Hao Sun, Hsin-Yu Lin, Jieqi Tu, Revathi Ananthakrishnan, Eunhee Kim

{"title":"Statistical operating characteristics of current early phase dose finding designs with toxicity and efficacy in oncology.","authors":"Hao Sun, Hsin-Yu Lin, Jieqi Tu, Revathi Ananthakrishnan, Eunhee Kim","doi":"10.1080/10543406.2024.2424845","DOIUrl":"https://doi.org/10.1080/10543406.2024.2424845","url":null,"abstract":"Traditional phase I dose finding cancer clinical trial designs aim to determine the maximum tolerated dose (MTD) of the investigational cytotoxic agent based on a single toxicity outcome, assuming a monotone dose-response relationship. However, this assumption might not always hold for newly emerging therapies such as immuno-oncology therapies and molecularly targeted therapies, making conventional dose finding trial designs based on toxicity no longer appropriate. To tackle this issue, numerous early-phase dose finding clinical trial designs have been developed to identify the optimal biological dose (OBD), which takes both toxicity and efficacy outcomes into account. In this article, we review the current model-assisted dose finding designs, BOIN-ET, BOIN12, UBI, TEPI-2, PRINTE, STEIN, and uTPI to identify the OBD and compare their operating characteristics. Extensive simulation studies and a case study using a CAR T-cell therapy phase I trial have been conducted to compare the performance of the aforementioned designs under different possible dose-response relationship scenarios. The simulation results demonstrate that the performance of different designs varies depending on the particular dose-response relationship and the specific metric considered. Based on our simulation results and practical considerations, STEIN, PRINTE, and BOIN12 outperform the other designs from different perspectives.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-21"},"PeriodicalIF":1.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645142","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

Machine learning approach for detection of MACE events within clinical trial data. 在临床试验数据中检测 MACE 事件的机器学习方法。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-11-16 DOI: 10.1080/10543406.2024.2420640

John A Spanias, Robbie Buderi, Pierre-Louis Bourlon, Christopher Tso, Caleb Strait, David Saunders, Kayleen Ports, Weixi Chen, Rahul Jain, Bhargav Koduru, Danielle Gerome, Eric Yang, Silvy Saltzmann, Aniketh Talwai, Tanmay Jain, Jacob Aptekar

{"title":"Machine learning approach for detection of MACE events within clinical trial data.","authors":"John A Spanias, Robbie Buderi, Pierre-Louis Bourlon, Christopher Tso, Caleb Strait, David Saunders, Kayleen Ports, Weixi Chen, Rahul Jain, Bhargav Koduru, Danielle Gerome, Eric Yang, Silvy Saltzmann, Aniketh Talwai, Tanmay Jain, Jacob Aptekar","doi":"10.1080/10543406.2024.2420640","DOIUrl":"https://doi.org/10.1080/10543406.2024.2420640","url":null,"abstract":"Randomized controlled trials (RCTs) are the gold standard for clinical research but may not accurately reflect the impact of medicines in real-world settings. Supplementing RCTs with insights from real-world data (RWD) can address known limitations by including more diverse patient populations, additional types of sites-of-care, and practices more representative of the care most people receive. One current challenge in using RWD is the lack of an algorithmic approach to identifying outcomes. To address this, machine learning models for identifying a frequently used outcome, Major Adverse Cardiovascular Events (MACE), were developed in Clinical Trial Data (CTD). Anonymized CTD sourced from the Medidata Enterprise Data Store were used to develop model features on the condition that they would be useful for labelling MACE events and that they could also be found in RWD. These features were used to train three random forest models to identify each component of 3-point MACE in a patient's clinical trial journey. Performance metrics for the models are presented (recall = 0.72 [0.07], precision = 0.68 [0.12] - mean, [SD]) along with the top contributing features. We show that the models can be tuned specifically to replicate the adjudication panels' results and present a cost-benefit analysis for deploying such models in clinical trial settings. We demonstrate the viability of using advanced algorithms for identifying clinical outcomes in prospective clinical trials. Deployment of such models could reduce the resources required to conduct RCTs. Extending such models to RWD would facilitate approval of pragmatic clinical trials for regulatory submissions.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645141","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

Defective regression models for cure rate data with competing risks. 具有竞争风险的治愈率数据的缺陷回归模型。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-11-14 DOI: 10.1080/10543406.2024.2424838

K Silpa, E P Sreedevi, P G Sankaran

引用次数: 0

An investigation to improve a nonlinear mixed-effects approach for EC50 estimation based on multi-donor dose-response data. 基于多供体剂量反应数据的 EC50 估算非线性混合效应方法的改进研究。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-11-06 DOI: 10.1080/10543406.2024.2421424

Weiliang Qiu, Cheng Wenren, Els Pattyn, Tamara Slavnic, Luc Esserméant

{"title":"An investigation to improve a nonlinear mixed-effects approach for EC50 estimation based on multi-donor dose-response data.","authors":"Weiliang Qiu, Cheng Wenren, Els Pattyn, Tamara Slavnic, Luc Esserméant","doi":"10.1080/10543406.2024.2421424","DOIUrl":"https://doi.org/10.1080/10543406.2024.2421424","url":null,"abstract":"Dose-response relationships are important in assessing the efficacy and potency of compounds, which can usually be characterized by a 4-parameter logistic (4-PL) model estimating EC50, slope factor, lower asymptote, and upper asymptote. EC50, the concentration of a compound that induces a response halfway between the baseline and maximum, is a key quantity to evaluate compound potency. For multi-donor dose-response data, it is often of interest to estimate the overall EC50 (i.e. the average EC50 of the population of donors) and its 95% confidence interval (CI). A few multi-donor EC50 estimation methods have been proposed in the literature. Jiang and Kopp-Schneider (2014) systematically compared the meta-analysis approach and the nonlinear mixed-effects approach and concluded that the meta-analysis approach is simple and robust to summarize EC50 estimates from multiple experiments, especially suited in the case of a small number of experiments, while the nonlinear mixed-effects approach has the issue of convergence failures probably due to overparameterization. In this article, we propose a modification of the nonlinear mixed-effects approach by using the stochastic approximation expectation-maximization (SAEM) algorithm to estimate model parameters and using multiple starting points to search for globally optimal values, which can substantially alleviate the issue of convergence failures even for small number of donors (e.g. n = 3), and achieve a smaller absolute median bias and better coverage probability of 95% confidence interval than the meta-analysis approach when the number of donors is not too small (e.g. n ≥ 7).","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583752","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

Large-scale dependent multiple testing via higher-order hidden Markov models. 通过高阶隐马尔可夫模型进行大规模依赖多重测试

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-11-04 DOI: 10.1080/10543406.2024.2420657

Canhui Li, Jiangzhou Wang, Pengfei Wang

引用次数: 0

Different view of the diagnostics test accuracy measures and optimal cut-off point selection procedure under tree or umbrella ordering. 在树状排序或伞状排序下，诊断检测准确度测量和最佳截断点选择程序的不同视角。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-30 DOI: 10.1080/10543406.2024.2420659

Jing Kersey, Hani Samawi, Marwan Alsharman, Mario Keko, Haresh Rochani, Lili Yu, Jingjing Yin, Kelly Sullivan, Salaheddin Mustafa

{"title":"Different view of the diagnostics test accuracy measures and optimal cut-off point selection procedure under tree or umbrella ordering.","authors":"Jing Kersey, Hani Samawi, Marwan Alsharman, Mario Keko, Haresh Rochani, Lili Yu, Jingjing Yin, Kelly Sullivan, Salaheddin Mustafa","doi":"10.1080/10543406.2024.2420659","DOIUrl":"https://doi.org/10.1080/10543406.2024.2420659","url":null,"abstract":"In the realm of medical diagnostic testing, diagnostic tests can assume either binary forms, distinguishing between diseased and non-diseased states, or ordinal forms, categorizing states from non-diseased to various stages (1 to K). Another significant classification scheme for multi-class scenarios is tree or umbrella ordering, which entails several unordered sub-classes (subtypes) within a biomarker. Within tree or umbrella ordering, the classifier assesses whether the marker measurement for one class surpasses or falls below those for the other classes. Although Receiver Operating Characteristic (ROC) curves and summary measures have been adapted to accommodate tree and umbrella ordering, these approaches often yield cut-off points that generate highly sensitive tests for certain disease subtypes while compromising specificity for others. This may not be ideal for all diseases. Hence, in this investigation, we explore diverse measures of diagnostic test accuracy and optimal cut-off point selection procedures under tree or umbrella ordering to foster more specific tests. We present numerical examples and simulation studies and demonstrate the approach using real data on lung cancer.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-31"},"PeriodicalIF":1.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549002","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

Small sample adjustment for inference without assuming orthogonality in a mixed model for repeated measures analysis. 在重复测量分析的混合模型中，无需假定正交性即可进行推理的小样本调整。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-29 DOI: 10.1080/10543406.2024.2420632

Kazushi Maruo, Ryota Ishii, Yusuke Yamaguchi, Tomohiro Ohigashi, Masahiko Gosho

{"title":"Small sample adjustment for inference without assuming orthogonality in a mixed model for repeated measures analysis.","authors":"Kazushi Maruo, Ryota Ishii, Yusuke Yamaguchi, Tomohiro Ohigashi, Masahiko Gosho","doi":"10.1080/10543406.2024.2420632","DOIUrl":"https://doi.org/10.1080/10543406.2024.2420632","url":null,"abstract":"The mixed model for repeated measures (MMRM) analysis is sometimes used as a primary statistical analysis for a longitudinal randomized clinical trial. When the MMRM analysis is implemented in ordinary statistical software, the standard error of the treatment effect is estimated by assuming orthogonality between the fixed effects and covariance parameters, based on the characteristics of the normal distribution. However, orthogonality does not hold unless the normality assumption of the error distribution holds, and/or the missing data are derived from the missing completely at random structure. Therefore, assuming orthogonality in the MMRM analysis is not preferable. However, without the assumption of orthogonality, the small-sample bias in the standard error of the treatment effect is significant. Nonetheless, there is no method to improve small-sample performance. Furthermore, there is no software that can easily implement inferences on treatment effects without assuming orthogonality. Hence, we propose two small-sample adjustment methods inflating standard errors that are reasonable in ideal situations and achieve empirical conservatism even in general situations. We also provide an R package to implement these inference processes. The simulation results show that one of the proposed small-sample adjustment methods performs particularly well in terms of underestimation bias of standard errors; consequently, the proposed method is recommended. When using the MMRM analysis, our proposed method is recommended if the sample size is not large and between-group heteroscedasticity is expected.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-15"},"PeriodicalIF":1.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549003","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 real-world data to conduct externally controlled trial for rare diseases with count-type endpoints: utilizing multiple entries - a simulation study. 利用真实世界的数据开展以计数型终点为对象的罕见病外部对照试验：利用多个条目--一项模拟研究。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-27 DOI: 10.1080/10543406.2024.2420644

Tianyu Sun, Eileen Liao, Nan Shao, Junxiang Luo

{"title":"Leveraging real-world data to conduct externally controlled trial for rare diseases with count-type endpoints: utilizing multiple entries - a simulation study.","authors":"Tianyu Sun, Eileen Liao, Nan Shao, Junxiang Luo","doi":"10.1080/10543406.2024.2420644","DOIUrl":"https://doi.org/10.1080/10543406.2024.2420644","url":null,"abstract":"Conducting randomized controlled trials for medications targeting rare diseases presents significant challenges, due to the scarcity of participants and ethical considerations. Under such circumstances, leveraging real-world data (RWD) to generate supporting evidence may be accepted by the regulatory agency. Constructing an external control arm (ECA) from RWD for a single-arm trial has been conducted occasionally. A complication in this design is that patients from RWD may be eligible at multiple time points. Most studies approach this by selecting one time point as the index date for ECA patients. Here, we propose a novel design for externally controlled trials that permits the inclusion of ECA patients at various entry points. Accompanying this design, we make recommendations for statistical methods to account for measured confounders, limited sample size, within-subject correlation, and potential overdispersion inherent in count data. Furthermore, we present an idea for the blinding process for this type of study. We have conducted a series of simulations to assess the performance of the design and statistical methods in terms of bias, type I error, and efficiency, as compared to the approach of selecting only one entry per ECA patient. The study and parameter setup were based on a hypothetical case inspired by a rare disease study. The results indicate that allowing multiple entries for ECA patients can lead to enhanced performance in many aspects. It provides a controlled type I error, robustness against certain model misspecifications, and a moderate power improvement compared with selecting a single entry per ECA patient.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-13"},"PeriodicalIF":1.2,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513228","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

Issues in cox proportional hazards model with unequal randomization. 采用不等随机化的考克斯比例危险模型的问题。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-24 DOI: 10.1080/10543406.2024.2418139

Hongfei Li, Qian H Li, Chuan Tian, Kevin Hou

引用次数: 0