IF 2.4 2区数学

Biometrika Pub Date : 2023-09-01 Epub Date: 2022-11-02 DOI: 10.1093/biomet/asac059

Yangjianchen Xu, Donglin Zeng, D Y Lin

{"title":"Marginal proportional hazards models for multivariate interval-censored data.","authors":"Yangjianchen Xu, Donglin Zeng, D Y Lin","doi":"10.1093/biomet/asac059","DOIUrl":"10.1093/biomet/asac059","url":null,"abstract":"Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption that all event times are independent, and we provide a simple and stable EM-type algorithm. The resulting nonparametric maximum pseudolikelihood estimators for the regression parameters are shown to be consistent and asymptotically normal, with a limiting covariance matrix that can be consistently estimated by a sandwich estimator under arbitrary dependence structures for the related event times. We evaluate the performance of the proposed methods through extensive simulation studies and present an application to data from the Atherosclerosis Risk in Communities Study.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"110 3","pages":"815-830"},"PeriodicalIF":2.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10434824/pdf/nihms-1874830.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10490393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 0

ASSESSING TIME-VARYING CAUSAL EFFECT MODERATION IN THE PRESENCE OF CLUSTER-LEVEL TREATMENT EFFECT HETEROGENEITY AND INTERFERENCE. 在集群水平治疗效果异质性和干扰存在的情况下评估时变因果效应调节。

IF 2.7 2区数学

Biometrika Pub Date : 2023-09-01 DOI: 10.1093/biomet/asac065

Jieru Shi, Zhenke Wu, Walter Dempsey

引用次数: 2

Sample-constrained partial identification with application to selection bias. 应用于选择偏差的样本约束部分识别。

IF 2.7 2区数学

Biometrika Pub Date : 2023-06-01 DOI: 10.1093/biomet/asac042

Matthew J Tudball, Rachael A Hughes, Kate Tilling, Jack Bowden, Qingyuan Zhao

{"title":"Sample-constrained partial identification with application to selection bias.","authors":"Matthew J Tudball, Rachael A Hughes, Kate Tilling, Jack Bowden, Qingyuan Zhao","doi":"10.1093/biomet/asac042","DOIUrl":"https://doi.org/10.1093/biomet/asac042","url":null,"abstract":"Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort studies. We show that existing sensitivity analyses, which are often conservative and difficult to implement, can be formulated in our framework and made significantly more informative via auxiliary information on the population. We conduct a simulation study to evaluate the finite sample performance of our inference procedure, and conclude with a substantive motivating example on the causal effect of education on income in the highly selected UK Biobank cohort. We demonstrate that our method can produce informative bounds using plausible population-level auxiliary constraints. We implement this method in the [Formula: see text] package [Formula: see text].","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"110 2","pages":"485-498"},"PeriodicalIF":2.7,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9914105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 4

Testing generalized linear models with high-dimensional nuisance parameter. 测试具有高维滋扰参数的广义线性模型

IF 2.7 2区数学

Biometrika Pub Date : 2023-03-01 Epub Date: 2022-04-05 DOI: 10.1093/biomet/asac021

Jinsong Chen, Quefeng Li, Hua Yun Chen

{"title":"Testing generalized linear models with high-dimensional nuisance parameter.","authors":"Jinsong Chen, Quefeng Li, Hua Yun Chen","doi":"10.1093/biomet/asac021","DOIUrl":"10.1093/biomet/asac021","url":null,"abstract":"Generalized linear models often have a high-dimensional nuisance parameters, as seen in applications such as testing gene-environment interactions or gene-gene interactions. In these scenarios, it is essential to test the significance of a high-dimensional sub-vector of the model's coefficients. Although some existing methods can tackle this problem, they often rely on the bootstrap to approximate the asymptotic distribution of the test statistic, and thus are computationally expensive. Here, we propose a computationally efficient test with a closed-form limiting distribution, which allows the parameter being tested to be either sparse or dense. We show that under certain regularity conditions, the type I error of the proposed method is asymptotically correct, and we establish its power under high-dimensional alternatives. Extensive simulations demonstrate the good performance of the proposed test and its robustness when certain sparsity assumptions are violated. We also apply the proposed method to Chinese famine sample data in order to show its performance when testing the significance of gene-environment interactions.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"110 1","pages":"83-99"},"PeriodicalIF":2.7,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9933885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10800040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 0

Significance testing for canonical correlation analysis in high dimensions. 高维度典型相关分析的显著性检验。

IF 2.4 2区数学

Biometrika Pub Date : 2022-12-01 Epub Date: 2022-11-18 DOI: 10.1093/biomet/asab059

Ian W McKeague, Xin Zhang

引用次数: 0

Functional hybrid factor regression model for handling heterogeneity in imaging studies. 处理影像学研究异质性的功能混合因子回归模型。

IF 2.7 2区数学

Biometrika Pub Date : 2022-12-01 DOI: 10.1093/biomet/asac007

C Huang, H Zhu

引用次数: 2

Graphical Gaussian Process Models for Highly Multivariate Spatial Data. 高多元空间数据的图形高斯过程模型。

IF 2.7 2区数学

Biometrika Pub Date : 2022-12-01 DOI: 10.1093/biomet/asab061

Debangan Dey, Abhirup Datta, Sudipto Banerjee

{"title":"Graphical Gaussian Process Models for Highly Multivariate Spatial Data.","authors":"Debangan Dey, Abhirup Datta, Sudipto Banerjee","doi":"10.1093/biomet/asab061","DOIUrl":"https://doi.org/10.1093/biomet/asab061","url":null,"abstract":"For multivariate spatial Gaussian process (GP) models, customary specifications of cross-covariance functions do not exploit relational inter-variable graphs to ensure process-level conditional independence among the variables. This is undesirable, especially for highly multivariate settings, where popular cross-covariance functions such as the multivariate Matérn suffer from a \"curse of dimensionality\" as the number of parameters and floating point operations scale up in quadratic and cubic order, respectively, in the number of variables. We propose a class of multivariate \"Graphical Gaussian Processes\" using a general construction called \"stitching\" that crafts cross-covariance functions from graphs and ensures process-level conditional independence among variables. For the Matérn family of functions, stitching yields a multivariate GP whose univariate components are Matérn GPs, and conforms to process-level conditional independence as specified by the graphical model. For highly multivariate settings and decomposable graphical models, stitching offers massive computational gains and parameter dimension reduction. We demonstrate the utility of the graphical Matérn GP to jointly model highly multivariate spatial data using simulation examples and an application to air-pollution modelling.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"109 4","pages":"993-1014"},"PeriodicalIF":2.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838617/pdf/nihms-1786615.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9104899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 17

On the relative efficiency of the intent-to-treat Wilcoxon-Mann-Whitney test in the presence of noncompliance. 在存在不依从性的情况下，治疗意图测试的相对效率。

IF 2.7 2区数学

Biometrika Pub Date : 2022-09-01 DOI: 10.1093/biomet/asab053

Lu Mao

{"title":"On the relative efficiency of the intent-to-treat Wilcoxon-Mann-Whitney test in the presence of noncompliance.","authors":"Lu Mao","doi":"10.1093/biomet/asab053","DOIUrl":"https://doi.org/10.1093/biomet/asab053","url":null,"abstract":"A general framework is set up to study the asymptotic properties of the intent-to-treat Wilcoxon-Mann-Whitney test in randomized experiments with nonignorable noncompliance. Under location-shift alternatives, the Pitman efficiencies of the intent-to-treat Wilcoxon-Mann-Whitney and [Formula: see text] tests are derived. It is shown that the former is superior if the compliers are more likely to be found in high-density regions of the outcome distribution or, equivalently, if the noncompliers tend to reside in the tails. By logical extension, the relative efficiency of the two tests is sharply bounded by least and most favourable scenarios in which the compliers are segregated into regions of lowest and highest density, respectively. Such bounds can be derived analytically as a function of the compliance rate for common location families such as Gaussian, Laplace, logistic and [Formula: see text] distributions. These results can help empirical researchers choose the more efficient test for existing data, and calculate sample size for future trials in anticipation of noncompliance. Results for nonadditive alternatives and other tests follow along similar lines.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"109 3","pages":"873-880"},"PeriodicalIF":2.7,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9401868/pdf/asab053.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10487820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 1

A multiplicative structural nested mean model for zero-inflated outcomes. 零膨胀结果的乘法结构嵌套均值模型。

IF 2.7 2区数学

Biometrika Pub Date : 2022-08-19 eCollection Date: 2023-06-01 DOI: 10.1093/biomet/asac050

Miao Yu, Wenbin Lu, Shu Yang, Pulak Ghosh

{"title":"A multiplicative structural nested mean model for zero-inflated outcomes.","authors":"Miao Yu, Wenbin Lu, Shu Yang, Pulak Ghosh","doi":"10.1093/biomet/asac050","DOIUrl":"10.1093/biomet/asac050","url":null,"abstract":"Zero-inflated nonnegative outcomes are common in many applications. In this work, motivated by freemium mobile game data, we propose a class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes which flexibly describes the joint effect of a sequence of treatments in the presence of time-varying confounders. The proposed estimator solves a doubly robust estimating equation, where the nuisance functions, namely the propensity score and conditional outcome means given confounders, are estimated parametrically or nonparametrically. To improve the accuracy, we leverage the characteristic of zero-inflated outcomes by estimating the conditional means in two parts, that is, separately modelling the probability of having positive outcomes given confounders, and the mean outcome conditional on its being positive and given the confounders. We show that the proposed estimator is consistent and asymptotically normal as either the sample size or the follow-up time goes to infinity. Moreover, the typical sandwich formula can be used to estimate the variance of treatment effect estimators consistently, without accounting for the variation due to estimating nuisance functions. Simulation studies and an application to a freemium mobile game dataset are presented to demonstrate the empirical performance of the proposed method and support our theoretical findings.","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"110 2","pages":"519-536"},"PeriodicalIF":2.7,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9841636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 0

Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring. 具有依赖性普查的生存结果的多阶段最佳动态治疗制度。

IF 2.7 2区数学

Biometrika Pub Date : 2022-08-13 eCollection Date: 2023-06-01 DOI: 10.1093/biomet/asac047

Hunyong Cho, Shannon T Holloway, David J Couper, Michael R Kosorok

引用次数: 0

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