Biometrika最新文献_第3页

A model-free variable screening method for optimal treatment regimes with high-dimensional survival data 利用高维生存数据优化治疗方案的无模型变量筛选法

IF 2.7 2区数学

Biometrika Pub Date : 2024-05-05 DOI: 10.1093/biomet/asae022

Cheng-Han Yang, Yu-Jen Cheng

{"title":"A model-free variable screening method for optimal treatment regimes with high-dimensional survival data","authors":"Cheng-Han Yang, Yu-Jen Cheng","doi":"10.1093/biomet/asae022","DOIUrl":"https://doi.org/10.1093/biomet/asae022","url":null,"abstract":"Summary We propose a model-free variable screening method for the optimal treatment regime with high-dimensional survival data. The proposed screening method provides a unified framework to select the active variables in a prespecified target population, including the treated group as a special case. Based on this framework, the optimal treatment regime is exactly the optimal classifier that minimizes a weighted misclassification error rate, with weights associated with survival outcome variables, the censoring distribution, and a prespecified target population. Our main contribution involves reformulating the weighted classification problem into a classification problem within a hypothetical population, where the observed data can be viewed as a sample obtained from outcome-dependent sampling, with the selection probability inversely proportional to the weights. Consequently, we introduce the weighted Kolmogorov–Smirnov approach for selecting active variables in the optimal treatment regime, extending the conventional Kolmogorov–Smirnov method for binary classification. Additionally, the proposed screening method exhibits two levels of robustness. The first level of robustness is achieved because the proposed method does not require any model assumptions for survival outcome on treatment and covariates, whereas the other is attained as the form of treatment regimes is allowed to be unspecified even without requiring convex surrogate loss, such as logit loss or hinge loss. As a result, the proposed screening method is robust to model misspecifications, and nonparametric learning methods such as random forests and boosting can be applied to those selected variables for further analysis. The theoretical properties of the proposed method are established. The performance of the proposed method is examined through simulation studies and illustrated by a real dataset.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"46 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140883240","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

Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes 对连续暴露和二元结果的匹配观察研究进行敏感性分析

IF 2.7 2区数学

Biometrika Pub Date : 2024-04-13 DOI: 10.1093/biomet/asae021

Jeffrey Zhang, Dylan S Small, Siyu Heng

{"title":"Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes","authors":"Jeffrey Zhang, Dylan S Small, Siyu Heng","doi":"10.1093/biomet/asae021","DOIUrl":"https://doi.org/10.1093/biomet/asae021","url":null,"abstract":"Summary Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be removed by matching. Therefore, a sensitivity analysis is typically needed to assess a causal conclusion’s sensitivity to unmeasured confounding. Sensitivity analysis frameworks for binary exposures have been well-established for various matching designs and are commonly used in various studies. However, unlike the binary exposure case, there still lacks valid and general sensitivity analysis methods for continuous exposures, except in some special cases such as pair matching. To fill this gap in the binary outcome case, we develop a sensitivity analysis framework for general matching designs with continuous exposures and binary outcomes. First, we use probabilistic lattice theory to show our sensitivity analysis approach is finite-population- exact under Fisher’s sharp null. Second, we prove a novel design sensitivity formula as a powerful tool for asymptotically evaluating the performance of our sensitivity analysis approach. Third, to allow effect heterogeneity with binary outcomes, we introduce a framework for conducting asymptotically exact inference and sensitivity analysis on generalized attributable effects with binary outcomes via mixed- integer programming. Fourth, for the continuous outcomes case, we show that conducting an asymptotically exact sensitivity analysis in matched observational studies when both the exposures and outcomes are continuous is generally NP-hard, except in some special cases such as pair matching. As a real data application, we apply our new methods to study the effect of early-life lead exposure on juvenile delinquency. An implementation of the methods in this work is available in the R package doseSens.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"1 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140568514","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

Sharp symbolic nonparametric bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes 具有序数结果的观察性研究和不完全随机研究中收益测量的锐利符号非参数界限

IF 2.7 2区数学

Biometrika Pub Date : 2024-04-11 DOI: 10.1093/biomet/asae020

Erin E Gabriel, Michael C Sachs, Andreas Kryger Jensen

引用次数: 0

Individualized dynamic model for multi-resolutional data 多分辨率数据的个性化动态模型

IF 2.7 2区数学

Biometrika Pub Date : 2024-04-08 DOI: 10.1093/biomet/asae015

J Zhang, F Xue, Q Xu, J Lee, A Qu

引用次数: 0

Flexible control of the median of the false discovery proportion 灵活控制错误发现比例的中位数

IF 2.7 2区数学

Biometrika Pub Date : 2024-03-23 DOI: 10.1093/biomet/asae018

Jesse Hemerik, Aldo Solari, Jelle J Goeman

引用次数: 0

Optimal regimes for algorithm-assisted human decision-making 算法辅助人类决策的最佳机制

IF 2.7 2区数学

Biometrika Pub Date : 2024-03-19 DOI: 10.1093/biomet/asae016

M J Stensrud, J D Laurendeau, A L Sarvet

引用次数: 0

Selective conformal inference with false coverage-statement rate control 带错误覆盖率控制的选择性保形推理

IF 2.7 2区数学

Biometrika Pub Date : 2024-02-19 DOI: 10.1093/biomet/asae010

Yajie Bao, Yuyang Huo, Haojie Ren, Changliang Zou

{"title":"Selective conformal inference with false coverage-statement rate control","authors":"Yajie Bao, Yuyang Huo, Haojie Ren, Changliang Zou","doi":"10.1093/biomet/asae010","DOIUrl":"https://doi.org/10.1093/biomet/asae010","url":null,"abstract":"Conformal inference is a popular tool for constructing prediction intervals. We consider here the scenario of post-selection/selective conformal inference, that is prediction intervals are reported only for individuals selected from unlabelled test data. To account for multiplicity, we develop a general split conformal framework to construct selective prediction intervals with the false coverage-statement rate control. We first investigate benjamini2005false's false coverage rate-adjusted method in the present setting, and show that it is able to achieve false coverage-statement rate control but yields uniformly inflated prediction intervals. We then propose a novel solution to the problem called selective conditional conformal prediction. Our method performs selection procedures on both the calibration set and test set, and then constructs conformal prediction intervals for the selected test candidates with the aid of conditional empirical distribution obtained by the post-selection calibration set. When the selection rule is exchangeable, we show that our proposed method can exactly control the false coverage-statement rate in a model-free and distribution-free guarantee. For non-exchangeable selection procedures involving the calibration set, we provide non-asymptotic bounds for the false coverage-statement rate under mild distributional assumptions. Numerical results confirm the effectiveness and robustness of our method in false coverage-statement rate control and show that it achieves more narrowed prediction intervals over existing methods across various settings.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"21 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139950365","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

The Promises of Parallel Outcomes 并行成果的承诺

IF 2.7 2区数学

Biometrika Pub Date : 2024-02-17 DOI: 10.1093/biomet/asae008

Ying Zhou, Dingke Tang, Dehan Kong, Linbo Wang

引用次数: 0

Doubly robust estimation under covariate-induced dependent left truncation 协变量诱发的依存左截断条件下的双稳健估计

IF 2.7 2区数学

Biometrika Pub Date : 2024-02-11 DOI: 10.1093/biomet/asae005

Yuyao Wang, Andrew Ying, Ronghui Xu

{"title":"Doubly robust estimation under covariate-induced dependent left truncation","authors":"Yuyao Wang, Andrew Ying, Ronghui Xu","doi":"10.1093/biomet/asae005","DOIUrl":"https://doi.org/10.1093/biomet/asae005","url":null,"abstract":"Summary In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of time-to-event, conventional methods adjusting for left truncation tend to rely on the quasi-independence assumption that the truncation time and the event time are independent on the observed region. This assumption is violated when there is dependence between the truncation time and the event time possibly induced by measured covariates. Inverse probability of truncation weighting can be used in this case, but it is sensitive to misspecification of the truncation model. In this work, we apply semiparametric theory to find the efficient influence curve of the expectation of an arbitrarily transformed survival time in the presence of covariate-induced dependent left truncation. We then use it to construct estimators that are shown to enjoy double-robustness properties. Our work represents the first attempt to construct doubly robust estimators in the presence of left truncation, which does not fall under the established framework of coarsened data where doubly robust approaches were developed. We provide technical conditions for the asymptotic properties that appear to not have been carefully examined in the literature for time-to-event data, and study the estimators via extensive simulation. We apply the estimators to two datasets from practice, with different right-censoring patterns.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"45 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769979","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

Regression analysis of group-tested current status data 对分组测试的现状数据进行回归分析

IF 2.7 2区数学

Biometrika Pub Date : 2024-02-08 DOI: 10.1093/biomet/asae006

Shuwei Li, Tao Hu, Lianming Wang, Christopher S McMahan, Joshua M Tebbs

{"title":"Regression analysis of group-tested current status data","authors":"Shuwei Li, Tao Hu, Lianming Wang, Christopher S McMahan, Joshua M Tebbs","doi":"10.1093/biomet/asae006","DOIUrl":"https://doi.org/10.1093/biomet/asae006","url":null,"abstract":"Summary Group testing is an effective way to reduce the time and cost associated with conducting large-scale screening for infectious diseases. Benefits are realized through testing pools formed by combining specimens, such as blood or urine, from different individuals. In some studies, individuals are assessed only once and a time-to-event endpoint is recorded, for example, the time until infection. Combining group testing with this type of endpoint results in group-tested current status data (?). To analyse these complex data, we propose methods which estimate a proportional hazards regression model based on test outcomes from measuring the pools. A sieve maximum likelihood estimation approach is developed that approximates the cumulative baseline hazard function with a piecewise constant function. To identify the sieve estimator, a computationally efficient expectation-maximization algorithm is derived by using data augmentation. Asymptotic properties of both the parametric and nonparametric components of the sieve estimator are then established by applying modern empirical process theory. Numerical results from simulation studies show that our proposed method performs nominally and has advantages over the corresponding estimation method based on individual testing results. We illustrate our work by analysing a chlamydia dataset collected by the State Hygienic Laboratory at the University of Iowa.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"25 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139770356","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