Biometrika最新文献_第2页

Sensitivity models and bounds under sequential unmeasured confounding in longitudinal studies 纵向研究中连续未测量混杂情况下的灵敏度模型和界限

IF 2.7 2区数学

Biometrika Pub Date : 2024-08-20 DOI: 10.1093/biomet/asae044

Zhiqiang Tan

引用次数: 0

Studies in the history of probability and statistics, LI: the first conditional logistic regression 概率论与统计学史研究，LI：第一个条件逻辑回归

IF 2.7 2区数学

Biometrika Pub Date : 2024-08-09 DOI: 10.1093/biomet/asae038

J A Hanley

引用次数: 0

Robust Covariate-Balancing Method in Learning Optimal Individualized Treatment Regimes 学习最佳个性化治疗方案的稳健协变量平衡法

IF 2.7 2区数学

Biometrika Pub Date : 2024-07-17 DOI: 10.1093/biomet/asae036

Canhui Li, Donglin Zeng, Wensheng Zhu

{"title":"Robust Covariate-Balancing Method in Learning Optimal Individualized Treatment Regimes","authors":"Canhui Li, Donglin Zeng, Wensheng Zhu","doi":"10.1093/biomet/asae036","DOIUrl":"https://doi.org/10.1093/biomet/asae036","url":null,"abstract":"Summary One of the most important problems in precision medicine is to find the optimal individualized treatment rule, which is designed to recommend treatment decisions and maximize overall clinical benefit to patients based on their individual characteristics. Typically, the expected clinical outcome is required to be estimated first, in which an outcome regression model or a propensity score model usually needs to be assumed for most of the existing statistical methods. However, if either model assumption is invalid, the estimated treatment regime is not reliable. In this article, we first define a contrast value function, which is the basis of the study for individualized treatment regimes. Then we construct a hybrid estimator of the contrast value function, by combining two types of estimation methods. We further propose a robust covariate-balancing estimator of the contrast value function by combining the inverse probability weighted method and matching method, which is based on the covariate balancing propensity score proposed by Imai and Ratkovic (2014). Theoretical results show that the proposed estimator is doubly robust, that is, it is consistent if either the propensity score model or the matching is correct. Based on a large number of simulation studies, we demonstrate that the proposed estimator outperforms existing methods. Lastly, the proposed method is illustrated through analysis of the SUPPORT study.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"337 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Causal inference with hidden mediators 隐性中介的因果推断

IF 2.7 2区数学

Biometrika Pub Date : 2024-07-13 DOI: 10.1093/biomet/asae037

AmirEmad Ghassami, Alan Yang, Ilya Shpitser, Eric Tchetgen Tchetgen

{"title":"Causal inference with hidden mediators","authors":"AmirEmad Ghassami, Alan Yang, Ilya Shpitser, Eric Tchetgen Tchetgen","doi":"10.1093/biomet/asae037","DOIUrl":"https://doi.org/10.1093/biomet/asae037","url":null,"abstract":"Summary Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators which are not observed, yet error prone proxies of the hidden mediators are measured. Specifically, (i) we establish causal hidden mediation analysis, which extends classical causal mediation analysis methods for identifying natural direct and indirect effects under no unmeasured confounding to a setting where the mediator of interest is hidden, but proxies of it are available. (ii) We establish a hidden front-door criterion, which extends the classical front-door criterion to allow for hidden mediators for which proxies are available. (iii) We show that the identification of a certain causal effect called population intervention indirect effect remains possible with hidden mediators in settings where challenges in (i) and (ii) might co-exist. We view (i)-(iii) as important steps towards the practical application of front-door criteria and mediation analysis as mediators are almost always measured with error and thus, the most one can hope for in practice is that the measurements are at best proxies of mediating mechanisms. We propose identification approaches for the parameters of interest in our considered models. For the estimation aspect, we propose an influence function-based estimation method and provide an analysis for the robustness of the estimators.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"249 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

More Power by Using Fewer Permutations 用更少的排列组合获得更大的能量

IF 2.7 2区数学

Biometrika Pub Date : 2024-07-10 DOI: 10.1093/biomet/asae031

Nick W Koning

引用次数: 0

Testing Independence for Sparse Longitudinal Data 测试稀疏纵向数据的独立性

IF 2.7 2区数学

Biometrika Pub Date : 2024-07-08 DOI: 10.1093/biomet/asae035

Changbo Zhu, Junwen Yao, Jane-Ling Wang

引用次数: 0

Semiparametric efficiency gains from parametric restrictions on propensity scores 倾向分数参数限制带来的半参数效率收益