Pharmaceutical Statistics最新文献

Pre-Posterior Distributions in Drug Development and Their Properties.

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-25 DOI: 10.1002/pst.2450

Andrew P Grieve

引用次数: 0

Bayesian Solutions for Assessing Differential Effects in Biomarker Positive and Negative Subgroups.

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-25 DOI: 10.1002/pst.2456

Dan Jackson, Fanni Zhang, Carl-Fredrik Burman, Linda Sharples

{"title":"Bayesian Solutions for Assessing Differential Effects in Biomarker Positive and Negative Subgroups.","authors":"Dan Jackson, Fanni Zhang, Carl-Fredrik Burman, Linda Sharples","doi":"10.1002/pst.2456","DOIUrl":"https://doi.org/10.1002/pst.2456","url":null,"abstract":"The number of clinical trials that include a binary biomarker in design and analysis has risen due to the advent of personalised medicine. This presents challenges for medical decision makers because a drug may confer a stronger effect in the biomarker positive group, and so be approved either in this subgroup alone or in the all-comer population. We develop and evaluate Bayesian methods that can be used to assess this. All our methods are based on the same statistical model for the observed data but we propose different prior specifications to express differing degrees of knowledge about the extent to which the treatment may be more effective in one subgroup than the other. We illustrate our methods using some real examples. We also show how our methodology is useful when designing trials where the size of the biomarker negative subgroup is to be determined. We conclude that our Bayesian framework is a natural tool for making decisions, for example, whether to recommend using the treatment in the biomarker negative subgroup where the treatment is less likely to be efficacious, or determining the number of biomarker positive and negative patients to include when designing a trial.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716656","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

Beyond the Fragility Index. 超越脆弱性指数。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-21 DOI: 10.1002/pst.2452

Piero Quatto, Enrico Ripamonti, Donata Marasini

{"title":"Beyond the Fragility Index.","authors":"Piero Quatto, Enrico Ripamonti, Donata Marasini","doi":"10.1002/pst.2452","DOIUrl":"https://doi.org/10.1002/pst.2452","url":null,"abstract":"The results of randomized clinical trials (RCTs) are frequently assessed with the fragility index (FI). Although the information provided by FI may supplement the p value, this indicator presents intrinsic weaknesses and shortcomings. In this article, we establish an analysis of fragility within a broader framework so that it can reliably complement the information provided by the p value. This perspective is named the analysis of strength. We first propose a new strength index (SI), which can be adopted in normal distribution settings. This measure can be obtained for both significance and nonsignificance and is straightforward to calculate, thus presenting compelling advantages over FI, starting from the presence of a threshold. The case of time-to-event outcomes is also addressed. Then, beyond the p value, we develop the analysis of strength using likelihood ratios from Royall's statistical evidence viewpoint. A new R package is provided for performing strength calculations, and a simulation study is conducted to explore the behavior of SI and the likelihood-based indicator empirically across different settings. The newly proposed analysis of strength is applied in the assessment of the results of three recent trials involving the treatment of COVID-19.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687990","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

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

Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes. 优化 2 型糖尿病 3 期临床试验的样本量确定。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-10-30 DOI: 10.1002/pst.2446

Alexander C Cambon, James Travis, Liping Sun, Jada Idokogi, Anna Kettermann

{"title":"Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes.","authors":"Alexander C Cambon, James Travis, Liping Sun, Jada Idokogi, Anna Kettermann","doi":"10.1002/pst.2446","DOIUrl":"https://doi.org/10.1002/pst.2446","url":null,"abstract":"An informed estimate of subject-level variance is a key determinate for accurate estimation of the required sample size for clinical trials. Evaluating completed adult Type 2 diabetes studies submitted to the FDA for accuracy of the variance estimate at the planning stage provides insights to inform the sample size requirements for future studies. From the U.S. Food and Drug Administration (FDA) database of new drug applications containing 14,106 subjects from 26 phase 3 randomized studies submitted to the FDA in support of drug approvals in adult type 2 diabetes studies reviewed between 2013 and 2017, we obtained estimates of subject-level variance for the primary endpoint-change in glycated hemoglobin (HbA1c) from baseline to 6 months. In addition, we used nine additional studies to examine the impact of clinically meaningful covariates on residual standard deviation and sample size re-estimation. Our analyses show that reduced sample sizes can be used without interfering with the validity of efficacy results for adult type 2 diabetes drug trials. This finding has implications for future research involving the adult type 2 diabetes population, including the potential to reduce recruitment period length and improve the timeliness of results. Furthermore, our findings could be utilized in the design of future endocrinology clinical trials.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142546679","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

Prediction Intervals for Overdispersed Poisson Data and Their Application in Medical and Pre-Clinical Quality Control. 过度分散泊松数据的预测区间及其在医疗和临床前质量控制中的应用

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-10-30 DOI: 10.1002/pst.2447

Max Menssen, Martina Dammann, Firas Fneish, David Ellenberger, Frank Schaarschmidt

{"title":"Prediction Intervals for Overdispersed Poisson Data and Their Application in Medical and Pre-Clinical Quality Control.","authors":"Max Menssen, Martina Dammann, Firas Fneish, David Ellenberger, Frank Schaarschmidt","doi":"10.1002/pst.2447","DOIUrl":"https://doi.org/10.1002/pst.2447","url":null,"abstract":"In pre-clinical and medical quality control, it is of interest to assess the stability of the process under monitoring or to validate a current observation using historical control data. Classically, this is done by the application of historical control limits (HCL) graphically displayed in control charts. In many applications, HCL are applied to count data, for example, the number of revertant colonies (Ames assay) or the number of relapses per multiple sclerosis patient. Count data may be overdispersed, can be heavily right-skewed and clusters may differ in cluster size or other baseline quantities (e.g., number of petri dishes per control group or different length of monitoring times per patient). Based on the quasi-Poisson assumption or the negative-binomial distribution, we propose prediction intervals for overdispersed count data to be used as HCL. Variable baseline quantities are accounted for by offsets. Furthermore, we provide a bootstrap calibration algorithm that accounts for the skewed distribution and achieves equal tail probabilities. Comprehensive Monte-Carlo simulations assessing the coverage probabilities of eight different methods for HCL calculation reveal, that the bootstrap calibrated prediction intervals control the type-1-error best. Heuristics traditionally used in control charts (e.g., the limits in Shewhart c- or u-charts or the mean ± 2 SD) fail to control a pre-specified coverage probability. The application of HCL is demonstrated based on data from the Ames assay and for numbers of relapses of multiple sclerosis patients. The proposed prediction intervals and the algorithm for bootstrap calibration are publicly available via the R package predint.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142546680","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

Treatment Effect Measures Under Nonproportional Hazards. 非比例危害下的治疗效果测量。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-10-27 DOI: 10.1002/pst.2449

Dan Jackson, Michael Sweeting, Rose Baker

引用次数: 0