Pharmaceutical Statistics最新文献_第7页

A case study: Assessing the efficacy of the revised dosage regimen via prediction model for recurrent event rate using biomarker data. 案例研究：利用生物标志物数据建立复发率预测模型，评估修订剂量方案的疗效。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-02-05 DOI: 10.1002/pst.2362

Ahrim Youn, Jiarui Chi, Yue Cui, Hui Quan

{"title":"A case study: Assessing the efficacy of the revised dosage regimen via prediction model for recurrent event rate using biomarker data.","authors":"Ahrim Youn, Jiarui Chi, Yue Cui, Hui Quan","doi":"10.1002/pst.2362","DOIUrl":"10.1002/pst.2362","url":null,"abstract":"In recently conducted phase III trials in a rare disease area, patients received monthly treatment at a high dose of the drug, which targets to lower a specific biomarker level, closely associated with the efficacy endpoint, to around 10% across patients. Although this high dose demonstrated strong efficacy, treatments were withheld due to the reports of serious adverse events. Dosing in these studies were later resumed at a reduced dosage which targets to lower the biomarker level to 15%-35% across patients. Two questions arose after this disruption. The first is whether the efficacy of this revised regimen as measured by the reduction in annualized event rate is adequate to support the continuation of the development and the second is whether the potential bias due to the loss of patients during this dosing gap process can be gauged. To address these questions, we built a prediction model that quantitatively characterizes biomarker vs. endpoint relationship and predicts efficacy at the 15%-35% range of the biomarker level using the available data from the original high dose. This model predicts favorable event rate in the target biomarker level and shows that the bias due to the loss of patients is limited. These results support the continued development of the revised regimen, however, given the limitation of the data available, this prediction is planned to be validated further when data under the revised regimen become available.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"570-584"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139692642","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

Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation. 预测新研究的亚组治疗效果：在制药公司开展数据挑战的动机、结果和经验。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-02-07 DOI: 10.1002/pst.2368

Björn Bornkamp, Silvia Zaoli, Michela Azzarito, Ruvie Martin, Carsten Philipp Müller, Conor Moloney, Giulia Capestro, David Ohlssen, Mark Baillie

引用次数: 0

Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence. 评估基于群体的轨迹建模方法在发现不同服药模式方面的性能。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-02-08 DOI: 10.1002/pst.2365

Awa Diop, Alind Gupta, Sabrina Mueller, Louis Dron, Ofir Harari, Heather Berringer, Vinusha Kalatharan, Jay J H Park, Miceline Mésidor, Denis Talbot

{"title":"Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence.","authors":"Awa Diop, Alind Gupta, Sabrina Mueller, Louis Dron, Ofir Harari, Heather Berringer, Vinusha Kalatharan, Jay J H Park, Miceline Mésidor, Denis Talbot","doi":"10.1002/pst.2365","DOIUrl":"10.1002/pst.2365","url":null,"abstract":"It is well known that medication adherence is critical to patient outcomes and can decrease patient mortality. The Pharmacy Quality Alliance (PQA) has recognized and identified medication adherence as an important indicator of medication-use quality. Hence, there is a need to use the right methods to assess medication adherence. The PQA has endorsed the proportion of days covered (PDC) as the primary method of measuring adherence. Although easy to calculate, the PDC has however several drawbacks as a method of measuring adherence. PDC is a deterministic approach that cannot capture the complexity of a dynamic phenomenon. Group-based trajectory modeling (GBTM) is increasingly proposed as an alternative to capture heterogeneity in medication adherence. The main goal of this paper is to demonstrate, through a simulation study, the ability of GBTM to capture treatment adherence when compared to its deterministic PDC analogue and to the nonparametric longitudinal K-means. A time-varying treatment was generated as a quadratic function of time, baseline, and time-varying covariates. Three trajectory models are considered combining a cat's cradle effect, and a rainbow effect. The performance of GBTM was compared to the PDC and longitudinal K-means using the absolute bias, the variance, the c-statistics, the relative bias, and the relative variance. For all explored scenarios, we find that GBTM performed better in capturing different patterns of medication adherence with lower relative bias and variance even under model misspecification than PDC and longitudinal K-means.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"511-529"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139703122","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 sample size calculation in drug interaction trials. 关于药物相互作用试验的样本量计算。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-02-14 DOI: 10.1002/pst.2367

Paul Meyvisch, Mitra Ebrahimpoor

引用次数: 0

On the relative conservativeness of Bayesian logistic regression method in oncology dose-finding studies. 论贝叶斯逻辑回归法在肿瘤剂量测定研究中的相对保守性。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-02-05 DOI: 10.1002/pst.2364

Cheng-Han Yang, Guanghui Cheng, Ruitao Lin

引用次数: 0

Transporting randomized trial results to estimate counterfactual survival functions in target populations. 传输随机试验结果，估算目标人群的反事实生存函数。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-01-17 DOI: 10.1002/pst.2354

Zhiqiang Cao, Youngjoo Cho, Fan Li

{"title":"Transporting randomized trial results to estimate counterfactual survival functions in target populations.","authors":"Zhiqiang Cao, Youngjoo Cho, Fan Li","doi":"10.1002/pst.2354","DOIUrl":"10.1002/pst.2354","url":null,"abstract":"When the distributions of treatment effect modifiers differ between a randomized trial and an external target population, the sample average treatment effect in the trial may be substantially different from the target population average treatment, and accurate estimation of the latter requires adjusting for the differential distribution of effect modifiers. Despite the increasingly rich literature on transportability, little attention has been devoted to methods for transporting trial results to estimate counterfactual survival functions in target populations, when the primary outcome is time to event and subject to right censoring. In this article, we study inverse probability weighting and doubly robust estimators to estimate counterfactual survival functions and the target average survival treatment effect in the target population, and provide their respective approximate variance estimators. We focus on a common scenario where the target population information is observed only through a complex survey, and elucidate how the survey weights can be incorporated into each estimator we considered. Simulation studies are conducted to examine the finite-sample performances of the proposed estimators in terms of bias, efficiency and coverage, under both correct and incorrect model specifications. Finally, we apply the proposed method to assess transportability of the results in the Action to Control Cardiovascular Risk in Diabetes-Blood Pressure (ACCORD-BP) trial to all adults with Diabetes in the United States.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"442-465"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139484744","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

Shrinkage priors for isotonic probability vectors and binary data modeling, with applications to dose-response modeling. 等效概率向量和二元数据建模的收缩先验，并应用于剂量反应建模。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-02-23 DOI: 10.1002/pst.2372

Philip S Boonstra, Daniel R Owen, Jian Kang

{"title":"Shrinkage priors for isotonic probability vectors and binary data modeling, with applications to dose-response modeling.","authors":"Philip S Boonstra, Daniel R Owen, Jian Kang","doi":"10.1002/pst.2372","DOIUrl":"10.1002/pst.2372","url":null,"abstract":"Motivated by the need to model dose-response or dose-toxicity curves in clinical trials, we develop a new horseshoe-based prior for Bayesian isotonic regression modeling a binary outcome against an ordered categorical predictor, where the probability of the outcome is assumed to be monotonically non-decreasing with the predictor. The set of differences between outcome probabilities in consecutive categories of the predictor is equipped with a multivariate prior having support over simplex. The Dirichlet distribution, which can be derived from a normalized sum of independent gamma-distributed random variables, is a natural choice of prior, but using mathematical and simulation-based arguments, we show that the resulting posterior is prone to underflow and other numerical instabilities, even under simple data configurations. We propose an alternative prior based on horseshoe-type shrinkage that is numerically more stable. We show that this horseshoe-based prior is not subject to the numerical instability seen in the Dirichlet/gamma-based prior and that the horseshoe-based posterior can estimate the underlying true curve more efficiently than the Dirichlet-based one. We demonstrate the use of this prior in a model predicting the occurrence of radiation-induced lung toxicity in lung cancer patients as a function of dose delivered to normal lung tissue. Our methodology is implemented in the R package isotonicBayes and therefore suitable for use in the design of dose-finding studies or other dose-response modeling contexts.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"540-556"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139940445","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

The flaw of averages: Bayes factors as posterior means of the likelihood ratio. 平均值的缺陷：贝叶斯因子作为似然比的后验手段。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-01-28 DOI: 10.1002/pst.2355

Charles C Liu, Ron Xiaolong Yu, Murray Aitkin

引用次数: 0

A generalized Bayesian optimal interval design for dose optimization in immunotherapy. 用于免疫疗法剂量优化的广义贝叶斯最优区间设计。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-01-31 DOI: 10.1002/pst.2369

Qing Xia, Kentaro Takeda, Yusuke Yamaguchi, Jun Zhang

{"title":"A generalized Bayesian optimal interval design for dose optimization in immunotherapy.","authors":"Qing Xia, Kentaro Takeda, Yusuke Yamaguchi, Jun Zhang","doi":"10.1002/pst.2369","DOIUrl":"10.1002/pst.2369","url":null,"abstract":"For novel immuno-oncology therapies, the primary purpose of a dose-finding trial is to identify an optimal dose (OD), defined as the tolerable dose having adequate efficacy and immune response under the unpredictable dose-outcome (toxicity, efficacy, and immune response) relationships. In addition, the multiple low or moderate-grade toxicities rather than dose-limiting toxicities (DLTs) and multiple levels of efficacy should be evaluated differently in dose-finding to determine true OD for developing novel immuno-oncology therapies. We proposed a generalized Bayesian optimal interval design for immunotherapy, simultaneously considering efficacy and toxicity grades and immune response outcomes. The proposed design, named gBOIN-ETI design, is model-assisted and easy to implement to develop immunotherapy efficiently. The operating characteristics of the gBOIN-ETI are compared with other dose-finding trial designs in oncology by simulation across various realistic settings. Our simulations show that the gBOIN-ETI design could outperform the other available approaches in terms of both the percentage of correct OD selection and the average number of patients allocated to the OD across various realistic trial settings.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"480-494"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139723543","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

The partnership between statisticians and the Institutional Animal Care and Use Committee (IACUC). 统计人员与机构动物护理和使用委员会（IACUC）之间的合作。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2024-06-11 DOI: 10.1002/pst.2390

David Potter, Thomas Bradstreet, Davit Sargsyan, Xiao Tan, Vinicius Bonato, Dingzhou Li, John Liang, Ondrej Libiger, Jocelyn Sendecki, John Stansfield, Kanaka Tatikola, Jialin Xu, Brandy Campbell

引用次数: 0