Journal of the Royal Statistical Society Series C-Applied Statistics最新文献_第7页

Statistical calibration for infinite many future values in linear regression: simultaneous or pointwise tolerance intervals or what else? 线性回归中无限多个未来值的统计校准:同步或点公差区间或其他什么?

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-02-13 DOI: 10.1093/jrsssc/qlac004

Yang Han, Yujia Sun, Lingjiao Wang, Wei Liu, F. Bretz

{"title":"Statistical calibration for infinite many future values in linear regression: simultaneous or pointwise tolerance intervals or what else?","authors":"Yang Han, Yujia Sun, Lingjiao Wang, Wei Liu, F. Bretz","doi":"10.1093/jrsssc/qlac004","DOIUrl":"https://doi.org/10.1093/jrsssc/qlac004","url":null,"abstract":"\u0000 Statistical calibration using regression is a useful statistical tool with many applications. For confidence sets for x-values associated with infinitely many future y-values, there is a consensus in the statistical literature that the confidence sets constructed should guarantee a key property. While it is well known that the confidence sets based on the simultaneous tolerance intervals (STIs) guarantee this key property conservatively, it is desirable to construct confidence sets that satisfy this property exactly. Also, there is a misconception that the confidence sets based on the pointwise tolerance intervals (PTIs) also guarantee this property. This paper constructs the weighted simultaneous tolerance intervals (WSTIs) so that the confidence sets based on the WSTIs satisfy this property exactly if the future observations have the x-values distributed according to a known specific distribution F(⋅). Through the lens of the WSTIs, convincing counter examples are also provided to demonstrate that the confidence sets based on the PTIs do not guarantee the key property in general and so should not be used. The WSTIs have been applied to real data examples to show that the WSTIs can produce more accurate calibration intervals than STIs and PTIs.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83880930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the competitive facility location problem with a Bayesian spatial interaction model 基于贝叶斯空间相互作用模型的竞争性设施选址问题

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-02-09 DOI: 10.1093/jrsssc/qlad003

Shanaka Perera, Virginia Aglietti, T. Damoulas

引用次数: 0

The determinants of Airbnb prices in New York City: a spatial quantile regression approach 纽约市Airbnb价格的决定因素:空间分位数回归方法

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-02-08 DOI: 10.1093/jrsssc/qlad001

M. Bernardi, M. Guidolin

引用次数: 0

Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer. 基于稀疏树的微生物组数据聚类，描述胰腺癌微生物组异质性的特征。

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-01-01 Epub Date: 2023-02-13 DOI: 10.1093/jrsssc/qlac002

Yushu Shi, Liangliang Zhang, Kim-Anh Do, Robert Jenq, Christine B Peterson

引用次数: 0

Utility-based Bayesian personalized treatment selection for advanced breast cancer 基于效用的晚期乳腺癌贝叶斯个性化治疗选择

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2022-12-22 DOI: 10.1111/rssc.12606

引用次数: 0

Contents of volume 71, 2022 第71卷内容，2022年

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2022-11-18 DOI: 10.1111/rssc.12605

引用次数: 0

Bayesian modelling strategies for borrowing of information in randomised basket trials 随机篮子试验中信息借鉴的贝叶斯建模策略。

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2022-10-28 DOI: 10.1111/rssc.12602

Luke O. Ouma, Michael J. Grayling, James M. S. Wason, Haiyan Zheng

{"title":"Bayesian modelling strategies for borrowing of information in randomised basket trials","authors":"Luke O. Ouma, Michael J. Grayling, James M. S. Wason, Haiyan Zheng","doi":"10.1111/rssc.12602","DOIUrl":"10.1111/rssc.12602","url":null,"abstract":"<p>Basket trials are an innovative precision medicine clinical trial design evaluating a single targeted therapy across multiple diseases that share a common characteristic. To date, most basket trials have been conducted in early-phase oncology settings, for which several Bayesian methods permitting information sharing across subtrials have been proposed. With the increasing interest of implementing randomised basket trials, information borrowing could be exploited in two ways; considering the commensurability of either the treatment effects or the outcomes specific to each of the treatment groups between the subtrials. In this article, we extend a previous analysis model based on distributional discrepancy for borrowing over the subtrial treatment effects (‘treatment effect borrowing’, TEB) to borrowing over the subtrial groupwise responses (‘treatment response borrowing’, TRB). Simulation results demonstrate that both modelling strategies provide substantial gains over an approach with no borrowing. TRB outperforms TEB especially when subtrial sample sizes are small on all operational characteristics, while the latter has considerable gains in performance over TRB when subtrial sample sizes are large, or the treatment effects and groupwise mean responses are noticeably heterogeneous across subtrials. Further, we notice that TRB, and TEB can potentially lead to different conclusions in the analysis of real data.</p>","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9827857/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9147756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Combining cytotoxic agents with continuous dose levels in seamless phase I-II clinical trials 在无缝I-II期临床试验中将细胞毒性药物与连续剂量水平相结合。

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2022-10-26 DOI: 10.1111/rssc.12598

José L. Jiménez, Mourad Tighiouart

{"title":"Combining cytotoxic agents with continuous dose levels in seamless phase I-II clinical trials","authors":"José L. Jiménez, Mourad Tighiouart","doi":"10.1111/rssc.12598","DOIUrl":"10.1111/rssc.12598","url":null,"abstract":"<p>Phase I-II cancer clinical trial designs are intended to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, it is common to divide the study in two stages: (i) a first stage in which dose is escalated based only on toxicity data and we look for the maximum tolerated dose (MTD) set and (ii) a second stage in which we search for the most efficacious dose within the MTD set. Current available approaches in the area of continuous dose levels involve fixing the MTD after stage I and discarding all collected stage I efficacy data. However, this methodology is clearly inefficient when there is a unique patient population present across stages. In this article, we propose a two-stage design for the combination of two cytotoxic agents assuming a single patient population across the entire study. In stage I, conditional escalation with overdose control is used to allocate successive cohorts of patients. In stage II, we employ an adaptive randomisation approach to allocate patients to drug combinations along the estimated MTD curve, which is constantly updated. The proposed methodology is assessed with extensive simulations in the context of a real case study.</p>","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9276318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Bayesian multi-level mixed-effects model for influenza dynamics 流感动力学的贝叶斯多级混合效应模型

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2022-10-24 DOI: 10.1111/rssc.12603

Hanwen Huang

引用次数: 0

Derivation of maternal dietary patterns accounting for regional heterogeneity 解释区域异质性的母体饮食模式的推导

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2022-10-18 DOI: 10.1111/rssc.12604

Briana J. K. Stephenson, Amy H. Herring, Andrew F. Olshan

引用次数: 3