Journal of Biopharmaceutical Statistics最新文献_第2页

Directed Acyclic Graph Assisted Method For Estimating Average Treatment Effect. 估算平均治疗效果的有向无环图辅助方法

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2023-12-27 DOI: 10.1080/10543406.2023.2296047

Jingchao Sun, Scott Duncan, Subhadip Pal, Maiying Kong

{"title":"Directed Acyclic Graph Assisted Method For Estimating Average Treatment Effect.","authors":"Jingchao Sun, Scott Duncan, Subhadip Pal, Maiying Kong","doi":"10.1080/10543406.2023.2296047","DOIUrl":"10.1080/10543406.2023.2296047","url":null,"abstract":"Observational data, such as electronic clinical records and claims data, can prove invaluable for evaluating the Average Treatment Effect (ATE) and supporting decision-making, provided they are employed correctly. The Inverse Probability of Treatment Weighting (IPTW) method, based on propensity scores, has demonstrated remarkable efficacy in estimating ATE, assuming that the assumptions of exchangeability, consistency, and positivity are met. Directed Acyclic Graphs (DAGs) offer a practical approach to assess the exchangeability assumption, which asserts that treatment assignment and potential outcomes are independent given a set of confounding variables that block all backdoor paths from treatment assignment to potential outcomes. To ensure a consistent ATE estimator, one can adjust for a minimally sufficient adjustment set of confounding variables that block all backdoor paths from treatment assignment to the outcome. To enhance the efficiency of ATE estimators, our proposal involves incorporating both the minimally sufficient adjustment set of confounding variables and predictors into the propensity score model. Extensive simulations were conducted to evaluate the performance of propensity score-based IPTW methods in estimating ATE when different sets of covariates were included in the propensity score models. The simulation results underscored the significance of including the minimally sufficient adjustment set of confounding variables along with predictors in the propensity score models to obtain a consistent and efficient ATE estimator. We applied this proposed method to investigate whether tracheostomy was causally associated with in-hospital infant mortality, utilizing the 2016 Healthcare Cost and Utilization Project Kids' Inpatient Database. The estimated ATE was found to be approximately 2.30%-2.46% with p-value >0.05.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"187-206"},"PeriodicalIF":1.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11209833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139049806","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

Bayesian phase II adaptive randomization by jointly modeling efficacy and toxicity as time-to-event outcomes. 通过将疗效和毒性联合建模为时间到事件结果，进行贝叶斯 II 期适应性随机化。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-01-01 DOI: 10.1080/10543406.2023.2297782

Yu-Mei Chang, Pao-Sheng Shen, Chun-Ying Ho

{"title":"Bayesian phase II adaptive randomization by jointly modeling efficacy and toxicity as time-to-event outcomes.","authors":"Yu-Mei Chang, Pao-Sheng Shen, Chun-Ying Ho","doi":"10.1080/10543406.2023.2297782","DOIUrl":"10.1080/10543406.2023.2297782","url":null,"abstract":"The main goals of Phase II trials are to identify the therapeutic efficacy of new treatments and continue monitoring all the possible adverse effects. In Phase II trials, it is important to develop an adaptive randomization (AR) procedure that takes into account both the efficacy and toxicity. In most existing articles, toxicity is modeled as a binary endpoint through an unobservable random effect (frailty) to link the efficacy and toxicity. However, this approach does not capture toxicity profiles that evolve over time. In this article, we propose a new Bayesian adaptive randomization (BAR) procedure using the covariate-adjusted efficacy-toxicity ratio (ETR) index, where efficacy and toxicity are jointly modelled as time-to-event (TTE) outcomes. Furthermore, we also propose early stopping rules for toxicity and futility such that inferior treatments can be dropped at earlier time of trial. Simulation results show that compared to the BAR procedures based solely on the efficacy and that based on TTE efficacy and binary toxicity outcomes, the proposed BAR procedure can better identify the difference in treatment toxicity such that it can assign more patients to the superior treatment arm under some scenarios.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"207-226"},"PeriodicalIF":1.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139075923","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

Sample size estimation for recurrent event data using multifrailty and multilevel survival models. 使用多变量和多层次生存模型估算复发事件数据的样本量。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-02-09 DOI: 10.1080/10543406.2024.2310306

Derek Dinart, Carine Bellera, Virginie Rondeau

{"title":"Sample size estimation for recurrent event data using multifrailty and multilevel survival models.","authors":"Derek Dinart, Carine Bellera, Virginie Rondeau","doi":"10.1080/10543406.2024.2310306","DOIUrl":"10.1080/10543406.2024.2310306","url":null,"abstract":"In epidemiology and clinical research, recurrent events refer to individuals who are likely to experience transient clinical events repeatedly over an observation period. Examples include hospitalizations in patients with heart failure, fractures in osteoporosis studies and the occurrence of new lesions in oncology. We provided an in-depth analysis of the sample size required for the analysis of recurrent time-to-event data using multifrailty or multilevel survival models. We covered the topic from the simple shared frailty model to models with hierarchical or joint frailties. We relied on a Wald-type test statistic to estimate the sample size assuming either a single or multiple endpoints. Simulations revealed that the sample size increased as heterogeneity increased. We also observed that it was more attractive to include more patients and reduce the duration of follow-up than to include fewer patients and increase the duration of follow-up to obtain the number of events required. Each model investigated can address the question of the number of subjects for recurrent events. However, depending on the research question, one model will be more suitable than another. We illustrated our methodology with the AFFIRM-AHF trial investigating the effect of intravenous ferric carboxymaltose in patients hospitalised for acute heart failure.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"241-256"},"PeriodicalIF":1.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139708580","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

Considerations for master protocols using external controls. 使用外部控制的主协议的注意事项。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-02-16 DOI: 10.1080/10543406.2024.2311248

Jie Chen, Xiaoyun Nicole Li, Chengxing Cindy Lu, Sammy Yuan, Godwin Yung, Jingjing Ye, Hong Tian, Jianchang Lin

{"title":"Considerations for master protocols using external controls.","authors":"Jie Chen, Xiaoyun Nicole Li, Chengxing Cindy Lu, Sammy Yuan, Godwin Yung, Jingjing Ye, Hong Tian, Jianchang Lin","doi":"10.1080/10543406.2024.2311248","DOIUrl":"10.1080/10543406.2024.2311248","url":null,"abstract":"There has been an increasing use of master protocols in oncology clinical trials because of its efficiency to accelerate cancer drug development and flexibility to accommodate multiple substudies. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple investigational products and/or subpopulations are studied under a single protocol. Master protocols can use external data and evidence (e.g. external controls) for treatment effect estimation, which can further improve efficiency of master protocol trials. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands, assessment of fit-for-use real-world data, and considerations for different types of master protocols. Similarities and differences between regular randomized controlled trials and master protocols when using external controls are discussed. A targeted learning-based causal roadmap is presented which constitutes three key steps: (1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing sensitivity analyses. Two illustrative examples for master protocols using external controls are discussed for their merits and possible improvement in causal effect estimation.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"297-319"},"PeriodicalIF":1.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139747780","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 F_β -score for medical diagnostics tests of binary diseases: proposing new measures of accuracy.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-02-27 DOI: 10.1080/10543406.2025.2469866

Marwan Alsharman, Hani Samawi, Jing Kersey, Divine Wanduku

{"title":"On Fβ -score for medical diagnostics tests of binary diseases: proposing new measures of accuracy.","authors":"Marwan Alsharman, Hani Samawi, Jing Kersey, Divine Wanduku","doi":"10.1080/10543406.2025.2469866","DOIUrl":"https://doi.org/10.1080/10543406.2025.2469866","url":null,"abstract":"Accurate differentiation between health states - diseased or non-diseased - is essential in clinical diagnostics. Optimal cut-off points, or thresholds used to classify test results, are crucial for precise diagnoses. This work introduces the Harmonic Mean of F-score and inverse F-score (HF), a novel metric for a balanced assessment of diagnostic accuracy. HF integrates Specificity (Sp) and Negative Predictive Value (NPV) into the Negative F-score (NFγ), ensuring a comprehensive evaluation of true negatives and negative test reliability. Prioritizing both true positives and true negatives, HF was used in optimal cut-off point estimation under binary disease classification. Simulation results revealed that the HF measure performed well, often surpassing established methods in specific settings. The HF measure and cut-off point selection criterion were applied to real-life data, showcasing its ability to provide a balanced evaluation of diagnostic accuracy. The HF measure frequently outperformed traditional metrics. The HF metric's flexibility, allowing parameter adjustments to accommodate diverse scenarios, enables researchers and clinicians to tailor its emphasis on specific aspects of diagnostic performance depending on the context.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-27"},"PeriodicalIF":1.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525256","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

Optimal dose selection in phase I/II dose finding trial with contextual bandits: a case study and practical recommendations.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-02-27 DOI: 10.1080/10543406.2025.2469877

Jixian Wang, Ram Tiwari

{"title":"Optimal dose selection in phase I/II dose finding trial with contextual bandits: a case study and practical recommendations.","authors":"Jixian Wang, Ram Tiwari","doi":"10.1080/10543406.2025.2469877","DOIUrl":"https://doi.org/10.1080/10543406.2025.2469877","url":null,"abstract":"Dose selection is a key decision to make in the early phase of drug development. Classical phase I/II dose-finding trials randomly assign a few doses and select the best among them. Response-adaptive assignment designs are more efficient but are still far from optimal. Recently, some researchers used machine learning (ML) methods such as contextual bandits (CB) to find the \"optimal\" dose and to investigate the asymptotic properties of the methods. We present a case study for oncology phase I/II dose-finding trial designs using Thompson sampling and Bayesian bootstrap for CB with either modeling clinical utility directly or jointly modeling efficacy and safety. We focus on practical questions such as the number of interim analyses to conduct and whether we should model the utility directly, jointly model efficacy and safety which compose the utility, or use a model independent approach such as multi-armed bandits, but not for a specific compound or tumor type. We also consider how to use weak informative prior information. We conducted an extensive simulation study and compared different combinations of design settings and modeling methods, under several feasible scenarios of the dose-response relationship. Based on simulation results, we make practical recommendations for the use of the proposed ML approach for phase I/II dose-finding trial designs.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-27"},"PeriodicalIF":1.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525170","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

Sample size determination for a study with variable follow-up time.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-02-26 DOI: 10.1080/10543406.2025.2469879

Guogen Shan, Yahui Zhang, Xinlin Lu, Yulin Li, Minggen Lu, Zhigang Li

{"title":"Sample size determination for a study with variable follow-up time.","authors":"Guogen Shan, Yahui Zhang, Xinlin Lu, Yulin Li, Minggen Lu, Zhigang Li","doi":"10.1080/10543406.2025.2469879","DOIUrl":"https://doi.org/10.1080/10543406.2025.2469879","url":null,"abstract":"For a study to detect the outcome change at the follow-up visit from baseline, the pre-test and post-test design is commonly used to assess the treatment-control difference. Several existing methods were developed for sample size calculation including the subtraction method, analysis of covariance (ANCOVA), and linear mixed model. The first two methods can be used when the follow-up time is the same as scheduled. Although the linear mixed model can analyze the repeated measures by including the actual visit time to account for the variability of the follow-up time, it often assumes a constant treatment-control difference at any follow-up time which may not be correct in practice. We propose to develop a new statistical model to compare the treatment-control difference at the planned follow-up time while controlling for the follow-up time variation. The spline functions are used to estimate the trajectories of the treatment arm and the control arm. We compared the performance of these methods with regards to type I error rate, statistical power, and sample size under various conditions. These four methods all control for the type I error rate. The new method and the ANCOVA method are often more powerful than the other two methods, and they have similar statistical power when a linear disease progression is satisfied. For a study with non-linear disease progression, the new method can be more powerful than the ANCOVA method. We used data from a completed Alzheimer's disease trial to illustrate the application of the proposed method.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517579","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

Analysis of continuous monitoring device data.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-02-16 DOI: 10.1080/10543406.2025.2460455

Jin Wang, Javier Cabrera, Davit Sargsyan, Kanaka Tatikola, Kwok-Leung Tsui

引用次数: 0

Bayesian model averaging for randomized dose optimization trials in multiple indications.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-02-10 DOI: 10.1080/10543406.2025.2450325

Wei Wei, Jianchang Lin

{"title":"Bayesian model averaging for randomized dose optimization trials in multiple indications.","authors":"Wei Wei, Jianchang Lin","doi":"10.1080/10543406.2025.2450325","DOIUrl":"https://doi.org/10.1080/10543406.2025.2450325","url":null,"abstract":"In oncology dose-finding trials, small cohorts of patients are often assigned to increasing dose levels, with the aim of determining the maximum tolerated dose. In the era of targeted agents, this practice has come under intense scrutiny as treating patients at doses beyond a certain level often results in increased off-target toxicity without significant gains in antitumor activity. Dose optimization for targeted agents becomes more challenging in proof-of-concept trials when the experimental treatment is tested in multiple indications of low prevalence and there is the need to characterize the dose-response relationship in each indication. To provide an alternative to the conventional \"more is better\" paradigm in oncology dose finding, we propose a Bayesian model averaging approach based on robust mixture priors (rBMA) for identifying the recommended phase III dose in randomized dose optimization studies conducted simultaneously in multiple indications. Compared to the dose optimization strategy which evaluates the dose-response relationship in each indication independently, we demonstrate the proposed approach can improve the accuracy of dose recommendation by learning across indications. The performance of the proposed approach in making the correct dose recommendation is examined based on systematic simulation studies.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-13"},"PeriodicalIF":1.2,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392508","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

Characterization of a credibility index.

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-02-09 DOI: 10.1080/10543406.2025.2456170

Piero Quatto, Enrico Ripamonti, Donata Marasini

{"title":"Characterization of a credibility index.","authors":"Piero Quatto, Enrico Ripamonti, Donata Marasini","doi":"10.1080/10543406.2025.2456170","DOIUrl":"https://doi.org/10.1080/10543406.2025.2456170","url":null,"abstract":"In recent years, the role of the p-value in applied research has been heavily scrutinized. Several new proposals have been put forward from a Bayesian viewpoint, including the analysis of credibility. By using the reverse Bayes theorem, and reasoning in terms of subverting the significance or the non-significance denoted by the p-value, this analysis provides the credibility, in a Bayesian sense, of an experimental result. We discuss a normalized indicator of credibility, namely <math><mi>C</mi></math>, a variant of the index <math><mover><mi>C</mi><mo>˜</mo></mover></math> (Quatto et al. J. Biopharm. Stat. 32, 308-329, 2022). This can be used to assess the degree of credibility of experimental results and can also be compared with a fixed threshold. The index is extended to the case of one-sided hypotheses. A simulation study is conducted to empirically assess the behavior of the index <math><mi>C</mi></math>. Two illustrative examples in the contexts of pharmacotherapy for COVID-19 and heart failure are presented. We then propose adopting the credibility index for meta-analyses, in which it can provide a suitable diagnostic value for modeling fixed and random effects.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384139","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