Pharmaceutical Statistics最新文献_第2页

Subgroup Identification Based on Quantitative Objectives. 基于量化目标的分组识别。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-17 DOI: 10.1002/pst.2455

Yan Sun, A S Hedayat

引用次数: 0

Potential Bias Models With Bayesian Shrinkage Priors for Dynamic Borrowing of Multiple Historical Control Data. 用于动态借用多个历史控制数据的贝叶斯收缩先验潜在偏差模型。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-17 DOI: 10.1002/pst.2453

Tomohiro Ohigashi, Kazushi Maruo, Takashi Sozu, Ryo Sawamoto, Masahiko Gosho

{"title":"Potential Bias Models With Bayesian Shrinkage Priors for Dynamic Borrowing of Multiple Historical Control Data.","authors":"Tomohiro Ohigashi, Kazushi Maruo, Takashi Sozu, Ryo Sawamoto, Masahiko Gosho","doi":"10.1002/pst.2453","DOIUrl":"https://doi.org/10.1002/pst.2453","url":null,"abstract":"When multiple historical controls are available, it is necessary to consider the conflicts between current and historical controls and the relationships among historical controls. One of the assumptions concerning the relationships between the parameters of interest of current and historical controls is known as the \"Potential biases.\" Within the \"Potential biases\" assumption, the differences between the parameters of interest of the current control and of each historical control are defined as \"potential bias parameters.\" We define a class of models called \"potential biases model\" that encompass several existing methods, including the commensurate prior. The potential bias model incorporates homogeneous historical controls by shrinking the potential bias parameters to zero. In scenarios where multiple historical controls are available, a method that uses a horseshoe prior was proposed. However, various other shrinkage priors are also available. In this study, we propose methods that apply spike-and-slab, Dirichlet-Laplace, and spike-and-slab lasso priors to the potential bias model. We conduct a simulation study and analyze clinical trial examples to compare the performances of the proposed and existing methods. The horseshoe prior and the three other priors make the strongest use of historical controls in the absence of heterogeneous historical controls and reduce the influence of heterogeneous historical controls in the presence of a few historical controls. Among these four priors, the spike-and-slab prior performed the best for heterogeneous historical controls.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648110","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 Model-Based Trial Design With a Randomization Scheme Considering Pharmacokinetics Exposure for Dose Optimization in Oncology. 基于模型的试验设计，考虑到药物动力学暴露的随机方案，用于肿瘤学剂量优化

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-17 DOI: 10.1002/pst.2454

Jun Zhang, Kentaro Takeda, Masato Takeuchi, Kanji Komatsu, Jing Zhu, Yusuke Yamaguchi

{"title":"A Model-Based Trial Design With a Randomization Scheme Considering Pharmacokinetics Exposure for Dose Optimization in Oncology.","authors":"Jun Zhang, Kentaro Takeda, Masato Takeuchi, Kanji Komatsu, Jing Zhu, Yusuke Yamaguchi","doi":"10.1002/pst.2454","DOIUrl":"https://doi.org/10.1002/pst.2454","url":null,"abstract":"The primary purpose of an oncology dose-finding trial for novel anticancer agents has been shifting from determining the maximum tolerated dose to identifying an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. In 2022, the FDA Oncology Center of Excellence initiated Project Optimus to reform the paradigm of dose optimization and dose selection in oncology drug development and issued a draft guidance. The guidance suggests that dose-finding trials include randomized dose-response cohorts of multiple doses and incorporate information on pharmacokinetics (PK) in addition to safety and efficacy data to select the OD. Furthermore, PK information could be a quick alternative to efficacy data to predict the minimum efficacious dose and decide the dose assignment. This article proposes a model-based trial design for dose optimization with a randomization scheme based on PK outcomes in oncology. A simulation study shows that the proposed design has advantages compared to the other designs in the percentage of correct OD selection and the average number of patients assigned to OD in various realistic settings.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142647796","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 Dynamic Model-Based Adaptive Design for Oncology Dose Optimization in Phase I/II Clinical Trials. 基于贝叶斯动态模型的自适应设计，用于 I/II 期临床试验中的肿瘤剂量优化。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-10 DOI: 10.1002/pst.2451

Yingjie Qiu, Mingyue Li

{"title":"A Bayesian Dynamic Model-Based Adaptive Design for Oncology Dose Optimization in Phase I/II Clinical Trials.","authors":"Yingjie Qiu, Mingyue Li","doi":"10.1002/pst.2451","DOIUrl":"https://doi.org/10.1002/pst.2451","url":null,"abstract":"With the development of targeted therapy, immunotherapy, and antibody-drug conjugates (ADCs), there is growing concern over the \"more is better\" paradigm developed decades ago for chemotherapy, prompting the US Food and Drug Administration (FDA) to initiate Project Optimus to reform dose optimization and selection in oncology drug development. For early-phase oncology trials, given the high variability from sparse data and the rigidity of parametric model specifications, we use Bayesian dynamic models to borrow information across doses with only vague order constraints. Our proposed adaptive design simultaneously incorporates toxicity and efficacy outcomes to select the optimal dose (OD) in Phase I/II clinical trials, utilizing Bayesian model averaging to address the uncertainty of dose-response relationships and enhance the robustness of the design. Additionally, we extend the proposed design to handle delayed toxicity and efficacy outcomes. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed method under various practical scenarios. The results demonstrate that the proposed designs have desirable operating characteristics. A trial example is presented to demonstrate the practical implementation of the proposed designs.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142625891","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 Policy Estimands for Continuous Outcomes Using Off-Treatment Sequential Multiple Imputation. 使用非治疗序列多重估算法估算连续结果的治疗政策估计值。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-08-04 DOI: 10.1002/pst.2411

Thomas Drury, Juan J Abellan, Nicky Best, Ian R White

{"title":"Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off-Treatment Sequential Multiple Imputation.","authors":"Thomas Drury, Juan J Abellan, Nicky Best, Ian R White","doi":"10.1002/pst.2411","DOIUrl":"10.1002/pst.2411","url":null,"abstract":"The estimands framework outlined in ICH E9 (R1) describes the components needed to precisely define the effects to be estimated in clinical trials, which includes how post-baseline 'intercurrent' events (IEs) are to be handled. In late-stage clinical trials, it is common to handle IEs like 'treatment discontinuation' using the treatment policy strategy and target the treatment effect on outcomes regardless of treatment discontinuation. For continuous repeated measures, this type of effect is often estimated using all observed data before and after discontinuation using either a mixed model for repeated measures (MMRM) or multiple imputation (MI) to handle any missing data. In basic form, both these estimation methods ignore treatment discontinuation in the analysis and therefore may be biased if there are differences in patient outcomes after treatment discontinuation compared with patients still assigned to treatment, and missing data being more common for patients who have discontinued treatment. We therefore propose and evaluate a set of MI models that can accommodate differences between outcomes before and after treatment discontinuation. The models are evaluated in the context of planning a Phase 3 trial for a respiratory disease. We show that analyses ignoring treatment discontinuation can introduce substantial bias and can sometimes underestimate variability. We also show that some of the MI models proposed can successfully correct the bias, but inevitably lead to increases in variance. We conclude that some of the proposed MI models are preferable to the traditional analysis ignoring treatment discontinuation, but the precise choice of MI model will likely depend on the trial design, disease of interest and amount of observed and missing data following treatment discontinuation.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1144-1155"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141889907","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

Covariate adjustment and estimation of difference in proportions in randomized clinical trials. 随机临床试验中的协变量调整和比例差异估算。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-05-19 DOI: 10.1002/pst.2397

Jialuo Liu, Dong Xi

{"title":"Covariate adjustment and estimation of difference in proportions in randomized clinical trials.","authors":"Jialuo Liu, Dong Xi","doi":"10.1002/pst.2397","DOIUrl":"10.1002/pst.2397","url":null,"abstract":"Difference in proportions is frequently used to measure treatment effect for binary outcomes in randomized clinical trials. The estimation of difference in proportions can be assisted by adjusting for prognostic baseline covariates to enhance precision and bolster statistical power. Standardization or g-computation is a widely used method for covariate adjustment in estimating unconditional difference in proportions, because of its robustness to model misspecification. Various inference methods have been proposed to quantify the uncertainty and confidence intervals based on large-sample theories. However, their performances under small sample sizes and model misspecification have not been comprehensively evaluated. We propose an alternative approach to estimate the unconditional variance of the standardization estimator based on the robust sandwich estimator to further enhance the finite sample performance. Extensive simulations are provided to demonstrate the performances of the proposed method, spanning a wide range of sample sizes, randomization ratios, and model specification. We apply the proposed method in a real data example to illustrate the practical utility.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"884-905"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141065823","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

Investigating Stability in Subgroup Identification for Stratified Medicine. 研究分层医疗亚组识别的稳定性。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-06-25 DOI: 10.1002/pst.2409

G M Hair, T Jemielita, S Mt-Isa, P M Schnell, R Baumgartner

{"title":"Investigating Stability in Subgroup Identification for Stratified Medicine.","authors":"G M Hair, T Jemielita, S Mt-Isa, P M Schnell, R Baumgartner","doi":"10.1002/pst.2409","DOIUrl":"10.1002/pst.2409","url":null,"abstract":"Subgroup analysis may be used to investigate treatment effect heterogeneity among subsets of the study population defined by baseline characteristics. Several methodologies have been proposed in recent years and with these, statistical issues such as multiplicity, complexity, and selection bias have been widely discussed. Some methods adjust for one or more of these issues; however, few of them discuss or consider the stability of the subgroup assignments. We propose exploring the stability of subgroups as a sensitivity analysis step for stratified medicine to assess the robustness of the identified subgroups besides identifying possible factors that may drive this instability. After applying Bayesian credible subgroups, a nonparametric bootstrap can be used to assess stability at subgroup-level and patient-level. Our findings illustrate that when the treatment effect is small or not so evident, patients are more likely to switch to different subgroups (jumpers) across bootstrap resamples. In contrast, when the treatment effect is large or extremely convincing, patients generally remain in the same subgroup. While the proposed subgroup stability method is illustrated through Bayesian credible subgroups method on time-to-event data, this general approach can be used with other subgroup identification methods and endpoints.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"945-958"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141458676","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

Futility Interim Analysis Based on Probability of Success Using a Surrogate Endpoint. 基于使用替代终点的成功概率的无用性中期分析。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-02 DOI: 10.1002/pst.2410

Ronan Fougeray, Loïck Vidot, Marco Ratta, Zhaoyang Teng, Donia Skanji, Gaëlle Saint-Hilary

{"title":"Futility Interim Analysis Based on Probability of Success Using a Surrogate Endpoint.","authors":"Ronan Fougeray, Loïck Vidot, Marco Ratta, Zhaoyang Teng, Donia Skanji, Gaëlle Saint-Hilary","doi":"10.1002/pst.2410","DOIUrl":"10.1002/pst.2410","url":null,"abstract":"In clinical trials with time-to-event data, the evaluation of treatment efficacy can be a long and complex process, especially when considering long-term primary endpoints. Using surrogate endpoints to correlate the primary endpoint has become a common practice to accelerate decision-making. Moreover, the ethical need to minimize sample size and the practical need to optimize available resources have encouraged the scientific community to develop methodologies that leverage historical data. Relying on the general theory of group sequential design and using a Bayesian framework, the methodology described in this paper exploits a documented historical relationship between a clinical \"final\" endpoint and a surrogate endpoint to build an informative prior for the primary endpoint, using surrogate data from an early interim analysis of the clinical trial. The predictive probability of success of the trial is then used to define a futility-stopping rule. The methodology demonstrates substantial enhancements in trial operating characteristics when there is a good agreement between current and historical data. Furthermore, incorporating a robust approach that combines the surrogate prior with a vague component mitigates the impact of the minor prior-data conflicts while maintaining acceptable performance even in the presence of significant prior-data conflicts. The proposed methodology was applied to design a Phase III clinical trial in metastatic colorectal cancer, with overall survival as the primary endpoint and progression-free survival as the surrogate endpoint.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"971-983"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141492960","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

Survival Analysis Without Sharing of Individual Patient Data by Using a Gaussian Copula. 使用高斯 Copula 进行生存分析而无需共享单个患者数据

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-07 DOI: 10.1002/pst.2415

Federico Bonofiglio

{"title":"Survival Analysis Without Sharing of Individual Patient Data by Using a Gaussian Copula.","authors":"Federico Bonofiglio","doi":"10.1002/pst.2415","DOIUrl":"10.1002/pst.2415","url":null,"abstract":"Cox regression and Kaplan-Meier estimations are often needed in clinical research and this requires access to individual patient data (IPD). However, IPD cannot always be shared because of privacy or proprietary restrictions, which complicates the making of such estimations. We propose a method that generates pseudodata replacing the IPD by only sharing non-disclosive aggregates such as IPD marginal moments and a correlation matrix. Such aggregates are collected by a central computer and input as parameters to a Gaussian copula (GC) that generates the pseudodata. Survival inferences are computed on the pseudodata as if it were the IPD. Using practical examples we demonstrate the utility of the method, via the amount of IPD inferential content recoverable by the GC. We compare GC to a summary-based meta-analysis and an IPD bootstrap distributed across several centers. Other pseudodata approaches are also considered. In the empirical results, GC approximates the utility of the IPD bootstrap although it might yield more conservative inferences and it might have limitations in subgroup analyses. Overall, GC avoids many legal problems related to IPD privacy or property while enabling approximation of common IPD survival analyses otherwise difficult to conduct. Sharing more IPD aggregates than is currently practiced could facilitate \"second purpose\"-research and relax concerns regarding IPD access.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1031-1044"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555242","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 Methods for Quality Tolerance Limit (QTL) Monitoring. 质量容限 (QTL) 监测的贝叶斯方法。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-08-09 DOI: 10.1002/pst.2427

J C Poythress, Jin Hyung Lee, Kentaro Takeda, Jun Liu

{"title":"Bayesian Methods for Quality Tolerance Limit (QTL) Monitoring.","authors":"J C Poythress, Jin Hyung Lee, Kentaro Takeda, Jun Liu","doi":"10.1002/pst.2427","DOIUrl":"10.1002/pst.2427","url":null,"abstract":"In alignment with the ICH guideline for Good Clinical Practice [ICH E6(R2)], quality tolerance limit (QTL) monitoring has become a standard component of risk-based monitoring of clinical trials by sponsor companies. Parameters that are candidates for QTL monitoring are critical to participant safety and quality of trial results. Breaching the QTL of a given parameter could indicate systematic issues with the trial that could impact participant safety or compromise the reliability of trial results. Methods for QTL monitoring should detect potential QTL breaches as early as possible while limiting the rate of false alarms. Early detection allows for the implementation of remedial actions that can prevent a QTL breach at the end of the trial. We demonstrate that statistically based methods that account for the expected value and variability of the data generating process outperform simple methods based on fixed thresholds with respect to important operating characteristics. We also propose a Bayesian method for QTL monitoring and an extension that allows for the incorporation of partial information, demonstrating its potential to outperform frequentist methods originating from the statistical process control literature.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1166-1180"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141907380","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