国际生物统计学会是一个促进统计和数学理论和方法在生物科学领域的发展和应用的国际协会，包括农业、生物医学科学和公共卫生、生态学、环境科学、林业和相关学科。协会欢迎统计学家、数学家、生物科学家和其他致力于跨学科努力推动生物科学信息收集和解释的人士成为会员。该协会赞助在世界各地举行的两年一度的国际生物识别会议；通过其国家团体和地区，它还赞助地区和地方会议。

The International Biometric Society is an international association dedicated to promoting the development and application of statistical and mathematical theories and methods in the field of biosciences. This includes areas such as agriculture, biomedical sciences, public health, ecology, environmental science, forestry, and related disciplines. The society welcomes statisticians, mathematicians, biologists, and other professionals committed to interdisciplinary efforts to advance the collection and interpretation of bioscience information. The society sponsors a biennial International Biometric Conference held worldwide, as well as regional and local meetings through its national and regional groups.

国际生物统计学会是一个致力于促进统计学和数学理论及方法在生物科学领域的应用与发展的国际性协会。该协会涵盖多个领域，包括农业、生物医学科学、公共卫生、生态学、环境科学、林业及相关学科。协会欢迎统计学家、数学家、生物科学家及其他致力于跨学科合作、推动生物科学信息收集与解释的专业人士加入。该协会每两年举办一次国际生物识别会议，并通过其国家和地区分支机构赞助地方和区域性会议。

Biometrics

We describe a family of random walk rules for the sequential allocation of dose levels to patients in a dose-response study, or phase I clinical trial. Patients are sequentially assigned the next higher, same, or next lower dose level according to some probability distribution, which may be determined by ethical considerations as well as the patient's response. It is shown that one can choose these probabilities in order to center dose level assignments unimodally around any target quantile of interest. Estimation of the quantile is discussed; the maximum likelihood estimator and its variance are derived under a two-parameter logistic distribution, and the maximum likelihood estimator is compared with other nonparametric estimators. Random walk rules have clear advantages: they are simple to implement, and finite and asymptotic distribution theory is completely worked out. For a specific random walk rule, we compute finite and asymptotic properties and give examples of its use in planning studies. Having the finite distribution theory available and tractable obviates the need for elaborate simulation studies to analyze the properties of the design. The small sample properties of our rule, as determined by exact theory, compare favorably to those of the continual reassessment method, determined by simulation.

Stephen D. Durham

Nancy Flournoy

William F. Rosenberger

A Random Walk Rule for Phase I Clinical Trials

I期临床试验的随机游走规则

Abstract Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a noninvasive neuroimaging technique measures changes in electrical voltage on the scalp induced by brain cortical activity. With its high temporal resolution, EEG has emerged as an increasingly useful tool to study brain connectivity. Challenges with modeling EEG signals of complex brain activity include interactions among unknown sources, low signal-to-noise ratio, and substantial between-subject heterogeneity. In this work, we propose a state space model that jointly analyzes multichannel EEG signals and learns dynamics of different sources corresponding to brain cortical activity. Our model borrows strength from spatially correlated measurements and uses low-dimensional latent states to explain all observed channels. The model can account for patient heterogeneity and quantify the effect of a subject's covariates on the latent space. The EM algorithm, Kalman filtering, and bootstrap resampling are used to fit the state space model and provide comparisons between patient diagnostic groups. We apply the developed approach to a case-control study of alcoholism and reveal significant attenuation of brain activity in response to visual stimuli in alcoholic subjects compared to healthy controls.

Qinxia Wang

Ji Meng Loh

Xiaofu He

Yuanjia Wang

A Latent State Space Model for Estimating Brain Dynamics from Electroencephalogram (EEG) Data

从脑电图(EEG)数据估计脑动力学的潜在状态空间模型

Summary Modern biomedical studies have yielded abundant survival data with high-throughput predictors. Variable screening is a crucial first step in analyzing such data, for the purpose of identifying predictive biomarkers, understanding biological mechanisms, and making accurate predictions. To nonparametrically quantify the relevance of each candidate variable to the survival outcome, we propose integrated powered density (IPOD), which compares the differences in the covariate-stratified distribution functions. The proposed new class of statistics, with a flexible weighting scheme, is general and includes the Kolmogorov statistic as a special case. Moreover, the method does not rely on rigid regression model assumptions and can be easily implemented. We show that our method possesses sure screening properties, and confirm the utility of the proposal with extensive simulation studies. We apply the method to analyze a multiple myeloma study on detecting gene signatures for cancer patients’ survival.

Hyokyoung G. Hong

Xuerong Chen

David C. Christiani

Yi Li

Integrated Powered Density: Screening Ultrahigh Dimensional Covariates with Survival Outcomes

综合功率密度：筛选超高维协变量与生存结果

Motivated by ultrahigh-dimensional biomarkers screening studies, we propose a model-free screening approach tailored to censored lifetime outcomes. Our proposal is built upon the introduction of a new measure, survival impact index (SII). By its design, SII sensibly captures the overall influence of a covariate on the outcome distribution, and can be estimated with familiar nonparametric procedures that do not require smoothing and are readily adaptable to handle lifetime outcomes under various censoring and truncation mechanisms. We provide large sample distributional results that facilitate the inference on SII in classical multivariate settings. More importantly, we investigate SII as an effective screener for ultrahigh-dimensional data, not relying on rigid regression model assumptions for real applications. We establish the sure screening property of the proposed SII-based screener. Extensive numerical studies are carried out to assess the performance of our method compared with other existing screening methods. A lung cancer microarray data is analyzed to demonstrate the practical utility of our proposals.

Jialiang Li

Qi Zheng

Limin Peng

Zhipeng Huang

Biom

Survival Impact Index and Ultrahigh-Dimensional Model-Free Screening with Survival Outcomes

生存影响指数与超高维无模型生存结果筛选

Abstract We consider inference problems for high-dimensional (HD) functional data with a dense number of T repeated measurements taken for a large number of p variables from a small number of n experimental units. The spatial and temporal dependence, high dimensionality, and dense number of repeated measurements pose theoretical and computational challenges. This paper has two aims; our first aim is to solve the theoretical and computational challenges in testing equivalence among covariance matrices from HD functional data. The second aim is to provide computationally efficient and tuning-free tools with guaranteed stochastic error control. The weak convergence of the stochastic process formed by the test statistics is established under the “large p, large T, and small n” setting. If the null is rejected, we further show that the locations of the change points can be estimated consistently. The estimator's rate of convergence is shown to depend on the data dimension, sample size, number of repeated measurements, and signal-to-noise ratio. We also show that our proposed computation algorithms can significantly reduce the computation time and are applicable to real-world data with a large number of HD-repeated measurements (e.g., functional magnetic resonance imaging (fMRI) data). Simulation results demonstrate both the finite sample performance and computational effectiveness of our proposed procedures. We observe that the empirical size of the test is well controlled at the nominal level, and the locations of multiple change points can be accurately identified. An application to fMRI data demonstrates that our proposed methods can identify event boundaries in the preface of the television series Sherlock. Code to implement the procedures is available in an R package named TechPhD.

Ping-Shou Zhong

Homogeneity Tests of Covariance for High-Dimensional Functional Data with Applications to Event Segmentation

高维函数数据的协方差同质性检验及其在事件分割中的应用

ABSTRACT A genetic Gaussian network of multiple phenotypes, constructed through the inverse matrix of the genetic correlation matrix, is informative for understanding the biological dependencies of the phenotypes. However, its estimation may be challenging because the genetic correlation estimates are biased due to estimation errors and idiosyncratic pleiotropy inherent in GWAS summary statistics. Here, we introduce a novel approach called estimation of genetic graph (EGG), which eliminates the estimation error bias and idiosyncratic pleiotropy bias with the same techniques used in multivariable Mendelian randomization. The genetic network estimated by EGG can be interpreted as shared common biological contributions between phenotypes, conditional on others. We use both simulations and real data to demonstrate the superior efficacy of our novel method in comparison with the traditional network estimators.

Yihe Yang

Noah Lorincz-Comi

Xiaofeng Zhu

Estimation of a genetic Gaussian network using GWAS summary data

使用GWAS汇总数据估计遗传高斯网络

ABSTRACT This study introduces an innovative cumulative link modeling (CLM) approach to monitor crop progress over large areas using remote sensing data. Two distinct models are developed, a fixed-effects CLM and a mixed-effects one that incorporates annual random effects to capture the inherent inter-seasonal variability. Inference is based on partial-likelihood with two law variations, the standard CLM based on the multinomial distribution and a novel one based on the product binomial distribution. Model performance is evaluated on eight crops, namely corn, oats, sorghum, soybeans, winter wheat, alfalfa, dry beans, and millet, using in-situ data from Nebraska, USA, spanning 20 years. The models utilize the predictive attributes of calendar time, thermal time, and the normalized difference vegetation index. The results demonstrate the wide applicability of this approach to different crops, providing large-scale predictions of crop progress and allowing the estimation of important agronomic parameters. To facilitate reproducibility, an ecosystem of R packages has been developed and made publicly accessible under the name Ages of Man. The packages can be utilized to implement the presented methodology in any area with this type of data, including the USA.

Ioannis Oikonomidis

Samis Trevezas

Cumulative link mixed-effects models in the service of remote sensing crop progress monitoring

遥感作物进度监测中的累积链接混合效应模型

ABSTRACT Analyses of cluster randomized trials (CRTs) can be complicated by informative missing outcome data. Methods such as inverse probability weighted generalized estimating equations have been proposed to account for informative missingness by weighing the observed individual outcome data in each cluster. These existing methods have focused on settings where missingness occurs at the individual level and each cluster has partially or fully observed individual outcomes. In the presence of missing clusters, for example, all outcomes from a cluster are missing due to drop-out of the cluster, these approaches ignore this cluster-level missingness and can lead to biased inference if the cluster-level missingness is informative. Informative missingness at multiple levels can also occur in CRTs with a multi-level structure where study participants are nested in subclusters such as healthcare providers, and the subclusters are nested in clusters such as clinics. In this paper, we propose new estimators for estimating the marginal treatment effect in CRTs accounting for missing outcome data at multiple levels based on weighted generalized estimating equations. We show that the proposed multi-level multiply robust estimator is consistent and asymptotically normally distributed provided that one of the multiple propensity score models postulated at each clustering level is correctly specified. We evaluate the performance of the proposed method through extensive simulations and illustrate its use with a CRT evaluating a Malaria risk-reduction intervention in rural Madagascar.

Chia-Rui Chang

Rui Wang

Estimating marginal treatment effect in cluster randomized trials with multi-level missing outcomes

估计多水平缺失结果的集群随机试验中的边际处理效应

ABSTRACT Motivated by the challenges in analyzing gut microbiome and metagenomic data, this work aims to tackle the issue of measurement errors in high-dimensional regression models that involve compositional covariates. This paper marks a pioneering effort in conducting statistical inference on high-dimensional compositional data affected by mismeasured or contaminated data. We introduce a calibration approach tailored for the linear log-contrast model. Under relatively lenient conditions regarding the sparsity level of the parameter, we have established the asymptotic normality of the estimator for inference. Numerical experiments and an application in microbiome study have demonstrated the efficacy of our high-dimensional calibration strategy in minimizing bias and achieving the expected coverage rates for confidence intervals. Moreover, the potential application of our proposed methodology extends well beyond compositional data, suggesting its adaptability for a wide range of research contexts.

Huali Zhao

Tianying Wang

Debiased high-dimensional regression calibration for errors-in-variables log-contrast models

去偏的高维回归校准用于含误差变量对数对比模型

ABSTRACT The sequential multiple assignment randomized trial (SMART) is the ideal study design for the evaluation of multistage treatment regimes, which comprise sequential decision rules that recommend treatments for a patient at each of a series of decision points based on their evolving characteristics. A common goal is to compare the set of so-called embedded regimes represented in the design on the basis of a primary outcome of interest. In the study of chronic diseases and disorders, this outcome is often a time to an event, and a goal is to compare the distributions of the time-to-event outcome associated with each regime in the set. We present a general statistical framework in which we develop a logrank-type test for comparison of the survival distributions associated with regimes within a specified set based on the data from a SMART with an arbitrary number of stages that allows incorporation of covariate information to enhance efficiency and can also be used with data from an observational study. The framework provides clarification of the assumptions required to yield a principled test procedure, and the proposed test subsumes or offers an improved alternative to existing methods. We demonstrate performance of the methods in a suite of simulation studies. The methods are applied to a SMART in patients with acute promyelocytic leukemia.

Abbreviation
Journal Impact	1.25
Quartiles(Global)	STATISTICS & PROBABILITY(Q2)

ISSN	0006-341X, 1541-0420
h-index	149

Publication Information	Publisher: Oxford University Press，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1965，Proportion of original research papers: 100.00%，Self Citation Rate:7.10%， Gold OA Rate: 24.52%
Average review cycle	网友分享经验：偏慢,4-8周
Average recruitment ratio	网友分享经验：容易

Biometrics

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