American Statistician最新文献_第2页

Data Science in Practice. 数据科学实践。

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-09 DOI: 10.1080/00031305.2025.2490304

Xiao Hui Tai

引用次数: 0

An Example to Illustrate Randomized Trial Estimands and Estimators 一个说明随机试验估计和估计的例子

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-05 DOI: 10.1080/00031305.2025.2468399

Linda J. Harrison, Sean S. Brummel

{"title":"An Example to Illustrate Randomized Trial Estimands and Estimators","authors":"Linda J. Harrison, Sean S. Brummel","doi":"10.1080/00031305.2025.2468399","DOIUrl":"https://doi.org/10.1080/00031305.2025.2468399","url":null,"abstract":"Recently, the International Conference on Harmonisation finalized an estimand framework for randomized trials that was adopted by regulatory bodies worldwide. The framework introduced five strategies for handling post-randomization events; namely the treatment policy, composite variable, while on treatment, hypothetical and principal stratum estimands. We describe an illustrative example to elucidate the difference between these five strategies for handling intercurrent events and provide an estimation technique for each. Specifically, we consider the intercurrent event of treatment discontinuation and introduce potential outcome notation to describe five estimands and corresponding estimators: (1) an intention-to-treat estimator of the total effect of a treatment policy; (2) an intention-to-treat estimator of a composite of the outcome and remaining on treatment; (3) a per-protocol estimator of the outcome in individuals observed to remain on treatment; (4) a g-computation estimator of a hypothetical scenario that all individuals remain on treatment; and (5) a principal stratum estimator of the treatment effect in individuals who would remain on treatment under the experimental condition. Additional insight is provided by defining situations where certain estimands are equal, and by studying the while on treatment strategy under repeated outcome measures. We highlight relevant causal inference literature to enable adoption in practice.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"27 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933336","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

Applied Machine Learning Using mlr3 in R 在R中使用mlr3应用机器学习

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-03 DOI: 10.1080/00031305.2025.2469923

Xueying Tang

引用次数: 0

Foundations of Data Science with Python 数据科学基础与Python

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-03 DOI: 10.1080/00031305.2025.2458850

Qing Wang

引用次数: 0

A New General Class of Discrete Bivariate Distributions Constructed by the Usual Stochastic Order 一类新的由通常随机阶构造的离散二元分布

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-03 DOI: 10.1080/00031305.2025.2486306

Min Ju Lee, Na Young Yoo, Ji Hwan Cha

{"title":"A New General Class of Discrete Bivariate Distributions Constructed by the Usual Stochastic Order","authors":"Min Ju Lee, Na Young Yoo, Ji Hwan Cha","doi":"10.1080/00031305.2025.2486306","DOIUrl":"https://doi.org/10.1080/00031305.2025.2486306","url":null,"abstract":"In this paper, we develop a new general class of discrete bivariate distributions that can model the effect of the so-called ‘load-sharing configuration’. Under such load-sharing configuration, after the failure of one component, the surviving component has to shoulder extra load, which eventually results in its failure at an earlier time than what is expected under the case of independence. To model such effect, in this paper, the residual lifetime of the surviving component is assumed to be shortened according to the usual stochastic order. We derive the joint probability mass function, the joint survival function and the marginal distributions. The identifiability of the proposed model is thoroughly investigated. We discuss a bivariate ageing property of the developed class. It will be seen that the obtained joint distribution can be expressed in terms of existing underlying distributions, which increases the applicability of the developed bivariate distributions. It will also be shown that the developed class has a high degree of flexibility in the sense that numerous families of distributions can be generated just by specifying different underlying distributions and different parameter functions for modeling stochastic dependence. Some specific families of discrete bivariate distributions which can be usefully applied in practice are obtained, and their usefulness is illustrated by some real data set analyses.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"44 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933217","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

Counternull Sets in Randomized Experiments 随机实验中的反空集

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-03 DOI: 10.1080/00031305.2024.2432884

M.-A. C. Bind, D. B. Rubin

{"title":"Counternull Sets in Randomized Experiments","authors":"M.-A. C. Bind, D. B. Rubin","doi":"10.1080/00031305.2024.2432884","DOIUrl":"https://doi.org/10.1080/00031305.2024.2432884","url":null,"abstract":"Consider a study whose primary results are “not statistically significant”. How often does it lead to the following published conclusion that “there is no effect of the treatment/exposure on the outcome”? We believe too often and that the requirement to report counternull values could help to avoid this! In statistical parlance, the null value of an estimand is a value that is distinguished in some way from other possible values, for example a value that indicates no difference between the general health status of those treated with a new drug versus a traditional drug. A counternull value is a nonnull value of that estimand that is supported by the same amount of evidence that supports the null value. Of course, such a definition depends critically on how “evidence” is defined. Here, we consider the context of a randomized experiment where evidence is summarized by the randomization-based p-value associated with a specified sharp null hypothesis. Consequently, a counternull value has the same p-value from the randomization test as does the null value; the counternull value is rarely unique, but rather comprises a set of values. We explore advantages to reporting a counternull set in addition to the p-value associated with a null value; a first advantage is pedagogical, in that reporting it avoids the mistake of implicitly accepting a not-rejected null hypothesis; a second advantage is that the effort to construct a counternull set can be scientifically helpful by encouraging thought about nonnull values of estimands. Two examples are used to illustrate these ideas.","PeriodicalId":50801,"journal":{"name":"American Statistician","volume":"1 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933219","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

Modern Data Visualization with R 现代数据可视化与R

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-03 DOI: 10.1080/00031305.2025.2459438

John M. Hoenig

引用次数: 0

Estimation of a Generalized Treatment Effect in a Control Group Versus Treatment Group Design 对照组与治疗组设计的一般治疗效果的估计

IF 1.8 4区数学

American Statistician Pub Date : 2025-04-03 DOI: 10.1080/00031305.2024.2422933

Daniel R. Jeske

引用次数: 0

Causal Inference with Complex Surveys: A Unified Perspective on Sample Selection and Exposure Selection 复杂调查的因果推论：样本选择和暴露选择的统一视角

IF 1.8 4区数学

American Statistician Pub Date : 2024-11-05 DOI: 10.1080/00031305.2024.2423814

Giovanni Nattino, Robert Ashmead, Bo Lu

引用次数: 0

Cross-validatory Z-Residual for Diagnosing Shared Frailty Models 用于诊断共享虚弱模型的交叉验证性 Z-Residual模型

IF 1.8 4区数学

American Statistician Pub Date : 2024-10-29 DOI: 10.1080/00031305.2024.2421370

Tingxuan Wu, Cindy Feng, Longhai Li

引用次数: 0