20 世纪重要的科学发展之一是统计推理和应用于人类健康研究的方法的爆炸式增长。例子包括推理的可能性方法、流行病学统计、临床试验、生存分析和统计遗传学的发展。公共卫生和生物医学研究中的实质性问题推动了统计方法的发展，这反过来又提高了我们从数据中得出有效推论的能力。生物统计学的目标是推进统计科学及其在人类健康和疾病问题上的应用，最终目标是促进公众健康。

One of the significant scientific developments of the 20th century is the rapid growth of statistical inference and its application methods in public health research. This includes advancements in likelihood methods, epidemiological statistics, clinical trials, survival analysis, and statistical genetics. Substantial issues in public health and biomedical research drive the development of statistical methods, which in turn enhance our ability to draw valid inferences from data. The goal of Biostatistics is to advance statistical science and its application to human health and disease issues, ultimately aiming to promote public health.

20 世纪重要的科学发展之一是统计推理及其在公共卫生研究中的应用方法的迅猛增长。这包括推理的可能性方法、流行病学统计、临床试验、生存分析和统计遗传学等领域的发展。公共卫生和生物医学研究中的重要问题推动了统计方法的进步，而这些进步又增强了我们从数据中得出有效推论的能力。生物统计学的目标是推动统计科学的发展及其在人类健康和疾病问题上的应用，最终旨在促进公众健康。

Biostatistics

Abstract We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm—the graphical lasso—that is remarkably fast: It solves a 1000-node problem (∼500000 parameters) in at most a minute and is 30–4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.

Jerome Friedman

Trevor Hastie

Robert Tibshirani

Sparse inverse covariance estimation with the graphical lasso

稀疏逆协方差估计与图拉索方法

Summary Traditional linear mediation analysis has inherent limitations when it comes to handling high-dimensional mediators. Particularly, accurately estimating and rigorously inferring mediation effects is challenging, primarily due to the intertwined nature of the mediator selection issue. Despite recent developments, the existing methods are inadequate for addressing the complex relationships introduced by confounders. To tackle these challenges, we propose a novel approach called DP2LM (Deep neural network-based Penalized Partially Linear Mediation). This approach incorporates deep neural network techniques to account for nonlinear effects in confounders and utilizes the penalized partially linear model to accommodate high dimensionality. Unlike most existing works that concentrate on mediator selection, our method prioritizes estimation and inference on mediation effects. Specifically, we develop test procedures for testing the direct and indirect mediation effects. Theoretical analysis shows that the tests maintain the Type-I error rate. In simulation studies, DP2LM demonstrates its superior performance as a modeling tool for complex data, outperforming existing approaches in a wide range of settings and providing reliable estimation and inference in scenarios involving a considerable number of mediators. Further, we apply DP2LM to investigate the mediation effect of DNA methylation on cortisol stress reactivity in individuals who experienced childhood trauma, uncovering new insights through a comprehensive analysis.

Shuoyang Wang

Yuan Huang

DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders

DP2LM：利用深度学习方法进行高维中介变量和复杂混杂因素的中介效应估计与假设检验

Summary Univariate spatio-temporal models for areal count data have received great attention in recent years for estimating risks. However, models for studying multivariate responses are less commonly used mainly due to the computational burden. In this article, multivariate spatio-temporal P-spline models are proposed to study different forms of violence against women. Modeling distinct crimes jointly improves the precision of estimates over univariate models and allows to compute correlations among them. The correlation between the spatial and the temporal patterns may suggest connections among the different crimes that will certainly benefit a thorough comprehension of this problem that affects millions of women around the world. The models are fitted using integrated nested Laplace approximations and are used to analyze four distinct crimes against women at district level in the Indian state of Maharashtra during the period 2001–2013.

Gonzalo Vicente

Tomás Goicoa

María Dolores Ugarte

Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women

多变量贝叶斯时空P样条模型用于分析针对女性的犯罪

Converging evidence indicates that the heterogeneity of cognitive profiles may arise through detectable alternations in brain functional connectivity. Despite an unprecedented opportunity to uncover neurobiological subtypes through clustering or subtyping analyses on multi-state functional connectivity, few existing approaches are applicable to accommodate the network topology and unique biological architecture. To address this issue, we propose an innovative Bayesian nonparametric network-variate clustering analysis to uncover subgroups of individuals with homogeneous brain functional network patterns under multiple cognitive states. In light of the existing neuroscience literature, we assume there are unknown state-specific modular structures within functional connectivity. Concurrently, we identify informative network features essential for defining subtypes. To further facilitate practical use, we develop a computationally efficient variational inference algorithm to approximate posterior inference with satisfactory estimation accuracy. Extensive simulations show the superiority of our method. We apply the method to the Adolescent Brain Cognitive Development (ABCD) study, and identify neurodevelopmental subtypes and brain sub-network phenotypes under each state to signal neurobiological heterogeneity, suggesting promising directions for further exploration and investigation in neuroscience.

Tianqi Chen

Hongyu Zhao

Chichun Tan

Todd Constable

Sarah Yip

Yize Zhao

Bayesian subtyping for multi-state brain functional connectome with application on preadolescent brain cognition

贝叶斯分型用于多状态脑功能连接组及其在青春期前脑认知中的应用

Abstract Data increasingly take the form of a multi-way array, or tensor, in several biomedical domains. Such tensors are often incompletely observed. For example, we are motivated by longitudinal microbiome studies in which several timepoints are missing for several subjects. There is a growing literature on missing data imputation for tensors. However, existing methods give a point estimate for missing values without capturing uncertainty. We propose a multiple imputation approach for tensors in a flexible Bayesian framework, that yields realistic simulated values for missing entries and can propagate uncertainty through subsequent analyses. Our model uses efficient and widely applicable conjugate priors for a CANDECOMP/PARAFAC (CP) factorization, with a separable residual covariance structure. This approach is shown to perform well with respect to both imputation accuracy and uncertainty calibration, for scenarios in which either single entries or entire fibers of the tensor are missing. For two microbiome applications, it is shown to accurately capture uncertainty in the full microbiome profile at missing timepoints and used to infer trends in species diversity for the population. Documented R code to perform our multiple imputation approach is available at https://github.com/lockEF/MultiwayImputation.

Ziren Jiang

Gen Li

Eric F Lock

BAMITA: Bayesian multiple imputation for tensor arrays

BAMITA：张量数组的贝叶斯多重插补

Missing data in multiple variables is a common issue. We investigate the applicability of the framework of graphical models for handling missing data to a complex longitudinal pharmacological study of children with HIV treated with an efavirenz-based regimen as part of the CHAPAS-3 trial. Specifically, we examine whether the causal effects of interest, defined through static interventions on multiple continuous variables, can be recovered (estimated consistently) from the available data only. So far, no general algorithms are available to decide on recoverability, and decisions have to be made on a case-by-case basis. We emphasize the sensitivity of recoverability to even the smallest changes in the graph structure, and present recoverability results for three plausible missingness-directed acyclic graphs (m-DAGs) in the CHAPAS-3 study, informed by clinical knowledge. Furthermore, we propose the concept of a "closed missingness mechanism": if missing data are generated based on this mechanism, an available case analysis is admissible for consistent estimation of any statistical or causal estimand, even if data are missing not at random. Both simulations and theoretical considerations demonstrate how, in the assumed MNAR setting of our study, a complete or available case analysis can be superior to multiple imputation, and estimation results vary depending on the assumed missingness DAG. Our analyses demonstrate an innovative application of missingness DAGs to complex longitudinal real-world data, while highlighting the sensitivity of the results with respect to the assumed causal model.

Anastasiia Holovchak

Helen McIlleron

Paolo Denti

Michael Schomaker

Recoverability of causal effects under presence of missing data: a longitudinal case study

缺失数据存在下因果效应的可恢复性：一项纵向案例研究

Abstract In life history analysis of data from cohort studies, it is important to address the process by which participants are identified and selected. Many health studies select or enrol individuals based on whether they have experienced certain health related events, for example, disease diagnosis or some complication from disease. Standard methods of analysis rely on assumptions concerning the independence of selection and a person’s prospective life history process, given their prior history. Violations of such assumptions are common, however, and can bias estimation of process features. This has implications for the internal and external validity of cohort studies, and for the transportabilty of results to a population. In this paper, we study failure time analysis by proposing a joint model for the cohort selection process and the failure process of interest. This allows us to address both independence assumptions and the transportability of study results. It is shown that transportability cannot be guaranteed in the absence of auxiliary information on the population. Conditions that produce dependent selection and types of auxiliary data are discussed and illustrated in numerical studies. The proposed framework is applied to a study of the risk of psoriatic arthritis in persons with psoriasis.

Richard J Cook

Jerald F Lawless

Selection processes, transportability, and failure time analysis in life history studies

生命史研究中的选择过程、可转移性和失效时间分析

Summary This paper introduces functional quantile principal component analysis (FQPCA), a dimensionality reduction technique that extends the concept of functional principal components analysis (FPCA) to the examination of participant-specific quantiles curves. Our approach borrows strength across participants to estimate patterns in quantiles, and uses participant-level data to estimate loadings on those patterns. As a result, FQPCA is able to capture shifts in the scale and distribution of data that affect participant-level quantile curves, and is also a robust methodology suitable for dealing with outliers, heteroscedastic data or skewed data. The need for such methodology is exemplified by physical activity data collected using wearable devices. Participants often differ in the timing and intensity of physical activity behaviors, and capturing information beyond the participant-level expected value curves produced by FPCA is necessary for a robust quantification of diurnal patterns of activity. We illustrate our methods using accelerometer data from the National Health and Nutrition Examination Survey, and produce participant-level 10%, 50%, and 90% quantile curves over 24 h of activity. The proposed methodology is supported by simulation results, and is available as an R package.

Abbreviation	Biostat
Journal Impact	1.87
Quartiles(Global)	STATISTICS & PROBABILITY(Q1)

ISSN	1465-4644, 1468-4357
h-index	93

Publication Information	Publisher: Oxford University Press，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2003，Proportion of original research papers: 100.00%，Self Citation Rate:0.00%， Gold OA Rate: 21.81%
Average review cycle	网友分享经验：较慢,6-12周
Average recruitment ratio	网友分享经验：较易

Biostatistics

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience

Sparse inverse covariance estimation with the graphical lasso

DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders

Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women

Bayesian subtyping for multi-state brain functional connectome with application on preadolescent brain cognition

BAMITA: Bayesian multiple imputation for tensor arrays

BAMITA: Bayesian multiple imputation for tensor arrays

Bayesian subtyping for multi-state brain functional connectome with application on preadolescent brain cognition

Recoverability of causal effects under presence of missing data: a longitudinal case study

Selection processes, transportability, and failure time analysis in life history studies

Functional quantile principal component analysis