Biometrika最新文献_第4页

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

Explicit solutions for the asymptotically-optimal bandwidth in cross-validation 交叉验证中渐近最优带宽的显式解法

IF 2.7 2区数学

Biometrika Pub Date : 2024-02-08 DOI: 10.1093/biomet/asae007

Karim M Abadir, Michel Lubrano

引用次数: 0

On the failure of the bootstrap for Chatterjee's rank correlation 关于查特吉秩相关自举法的失败

IF 2.7 2区数学

Biometrika Pub Date : 2024-02-04 DOI: 10.1093/biomet/asae004

Zhexiao Lin, Fang Han

引用次数: 0

Asymptotically constant risk estimator of the time-average variance constant 时间平均方差常数的渐近恒定风险估计器

IF 2.7 2区数学

Biometrika Pub Date : 2024-02-03 DOI: 10.1093/biomet/asae003

K W Chan, C Y Yau

引用次数: 0

A note on minimax robustness of designs against correlated or heteroscedastic responses 关于针对相关或异方差响应的最小稳健性设计的说明

IF 2.7 2区数学

Biometrika Pub Date : 2024-01-20 DOI: 10.1093/biomet/asae001

D P Wiens

引用次数: 0

Efficient nonparametric estimation of Toeplitz covariance matrices 托普利兹协方差矩阵的高效非参数估计

IF 2.7 2区数学

Biometrika Pub Date : 2024-01-17 DOI: 10.1093/biomet/asae002

K Klockmann, T Krivobokova

引用次数: 0

On Selecting and Conditioning in Multiple Testing and Selective Inference 论多重测试和选择性推理中的选择和条件限制

IF 2.7 2区数学

Biometrika Pub Date : 2023-12-22 DOI: 10.1093/biomet/asad078

Jelle J Goeman, Aldo Solari

{"title":"On Selecting and Conditioning in Multiple Testing and Selective Inference","authors":"Jelle J Goeman, Aldo Solari","doi":"10.1093/biomet/asad078","DOIUrl":"https://doi.org/10.1093/biomet/asad078","url":null,"abstract":"We investigate a class of methods for selective inference that condition on a selection event. Such methods follow a two-stage process. First, a data-driven collection of hypotheses is chosen from some large universe of hypotheses. Subsequently, inference takes place within this data-driven collection, conditioned on the information that was used for the selection. Examples of such methods include basic data splitting, as well as modern data carving methods and post-selection inference methods for lasso coefficients based on the polyhedral lemma. In this paper, we adopt a holistic view on such methods, considering the selection, conditioning, and final error control steps together as a single method. From this perspective, we demonstrate that multiple testing methods defined directly on the full universe of hypotheses are always at least as powerful as selective inference methods based on selection and conditioning. This result holds true even when the universe is potentially infinite and only implicitly defined, such as in the case of data splitting. We give general theory and intuitions before investigating in detail several case studies where a shift to a non-selective or unconditional perspective can yield a power gain.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"94 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139051164","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

Central limit theorems for local network statistics 本地网络统计的中心极限定理

IF 2.7 2区数学

Biometrika Pub Date : 2023-12-22 DOI: 10.1093/biomet/asad080

P A Maugis

引用次数: 0

The state of cumulative sum sequential change point testing seventy years after Page 累积和顺序变化点测试七十年后的状况 Page

IF 2.7 2区数学

Biometrika Pub Date : 2023-12-21 DOI: 10.1093/biomet/asad079

Alexander Aue, Claudia Kirch

{"title":"The state of cumulative sum sequential change point testing seventy years after Page","authors":"Alexander Aue, Claudia Kirch","doi":"10.1093/biomet/asad079","DOIUrl":"https://doi.org/10.1093/biomet/asad079","url":null,"abstract":"\u0000 Quality control charts aim at raising an alarm as soon as sequentially obtained observations of an underlying random process no longer seem to be within stochastic fluctuations prescribed by an ‘in-control’ scenario. Such random processes can often be modelled using the concept of stationarity, or even independence as in most classical works. An important out-of-control scenario is the changepoint alternative, for which the distribution of the process changes at an unknown point in time. In his seminal 1954 Biometrika paper, E. S. Page introduced the famous cumulative sum control charts for changepoint monitoring. Innovatively, decision rules based on cumulative sum procedures took the full history of the process into account, whereas previous procedures were based only on a fixed and typically small number of the most recent observations. The extreme case of using only the most recent observation, often referred to as the Shewhart chart, is more akin to serial outlier than changepoint detection. Page’s cumulative sum approach, introduced seven decades ago, is ubiquitous in modern changepoint analysis, and his original paper has led to a multitude of follow-up papers in different research communities. This review is focused on a particular subfield of this research, namely nonparametric sequential, or online, changepoint tests which are constructed to maintain a desired Type 1 error as opposed to the more traditional approach seeking to minimize the average run length of the procedures. Such tests have originated at the intersection of econometrics and statistics. We trace the development of these tests and highlight their properties, mostly using a simple location model for clarity of exposition, but also review more complex situations such as regression and time series models.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"12 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951837","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