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Correction to “Robust approach to combining multiple markers to improve surrogacy” Biometrics (IF 1.9) Pub Date : 2023-12-19
Wang, X., Parast, L., Han, L., Tian, L. and Cai, T. (2023).Robust approach to combining multiple markers to improve surrogacy. Biometrics, 79: 788–798. https://doi.org/10.1111/biom.13677 In the original article, the Acknowledgments section was not included. It is included below.
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Multiresolution categorical regression for interpretable cell-type annotation Biometrics (IF 1.9) Pub Date : 2023-10-05 Aaron J. Molstad, Keshav Motwani
In many categorical response regression applications, the response categories admit a multiresolution structure. That is, subsets of the response categories may naturally be combined into coarser response categories. In such applications, practitioners are often interested in estimating the resolution at which a predictor affects the response category probabilities. In this paper, we propose a method
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SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials Biometrics (IF 1.9) Pub Date : 2023-09-18 Peng Yang, Yuansong Zhao, Lei Nie, Jonathon Vallejo, Ying Yuan
Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a noninformative prior. However, prespecifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the degree of prior-data conflict, which is often unknown beforehand, posing a significant
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Regression-based multiple treatment effect estimation under covariate-adaptive randomization Biometrics (IF 1.9) Pub Date : 2023-09-12 Yujia Gu, Hanzhong Liu, Wei Ma
Covariate-adaptive randomization methods are widely used in clinical trials to balance baseline covariates. Recent studies have shown the validity of using regression-based estimators for treatment effects without imposing functional form requirements on the true data generation model. These studies have had limitations in certain scenarios; for example, in the case of multiple treatment groups, these
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Latent factor model for multivariate functional data Biometrics (IF 1.9) Pub Date : 2023-09-04 Ruonan Li, Luo Xiao
For multivariate functional data, a functional latent factor model is proposed, extending the traditional latent factor model for multivariate data. The proposed model uses unobserved stochastic processes to induce the dependence among the different functions, and thus, for a large number of functions, may provide a more parsimonious and interpretable characterization of the otherwise complex dependencies
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Optimizing treatment allocation in randomized clinical trials by leveraging baseline covariates Biometrics (IF 1.9) Pub Date : 2023-08-28 Wei Zhang, Zhiwei Zhang, Aiyi Liu
We consider the problem of optimizing treatment allocation for statistical efficiency in randomized clinical trials. Optimal allocation has been studied previously for simple treatment effect estimators such as the sample mean difference, which are not fully efficient in the presence of baseline covariates. More efficient estimators can be obtained by incorporating covariate information, and modern
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Functional Bayesian networks for discovering causality from multivariate functional data Biometrics (IF 1.9) Pub Date : 2023-08-28 Fangting Zhou, Kejun He, Kunbo Wang, Yanxun Xu, Yang Ni
Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest. In this paper, we develop a novel Bayesian network (BN) model for multivariate functional data where conditional independencies and causal structure are encoded by a directed acyclic graph. Specifically, we allow the functional
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Study design for restricted mean time analysis of recurrent events and death Biometrics (IF 1.9) Pub Date : 2023-08-23 Lu Mao
The restricted mean time in favor (RMT-IF) of treatment has just been added to the analytic toolbox for composite endpoints of recurrent events and death. To help practitioners design new trials based on this method, we develop tools to calculate the sample size and power. Specifically, we formulate the outcomes as a multistate Markov process with a sequence of transient states for recurrent events
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Generating designs for comparative experiments with two blocking factors Biometrics (IF 1.9) Pub Date : 2023-08-18 Nha Vo-Thanh, Hans-Peter Piepho
Often, comparative experiments involve a single treatment factor and two blocking factors, for example, augmented row–column, two-phase, and incomplete row–column experiments. These experiments are widely used in agriculture. Finding good designs for these experiments is a major challenge when the number of treatments is large and the blocking structure is complex. In this paper, we first propose a
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How to analyze continuous and discrete repeated measures in small-sample cross-over trials? Biometrics (IF 1.9) Pub Date : 2023-08-16 Johan Verbeeck, Martin Geroldinger, Konstantin Thiel, Andrew Craig Hooker, Sebastian Ueckert, Mats Karlsson, Arne Cornelius Bathke, Johann Wolfgang Bauer, Geert Molenberghs, Georg Zimmermann
To optimize the use of data from a small number of subjects in rare disease trials, an at first sight advantageous design is the repeated measures cross-over design. However, it is unclear how these within-treatment period and within-subject clustered data are best analyzed in small-sample trials. In a real-data simulation study based upon a recent epidermolysis bullosa simplex trial using this design
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A second evidence factor for a second control group Biometrics (IF 1.9) Pub Date : 2023-08-10 Paul R. Rosenbaum
In an observational study of the effects caused by a treatment, a second control group is used in an effort to detect bias from unmeasured covariates, and the investigator is content if no evidence of bias is found. This strategy is not entirely satisfactory: two control groups may differ significantly, yet the difference may be too small to invalidate inferences about the treatment, or the control
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A proportional incidence rate model for aggregated data to study the vaccine effectiveness against COVID-19 hospital and ICU admissions Biometrics (IF 1.9) Pub Date : 2023-08-10 Ping Yan, Muhammad Abu Shadeque Mullah, Ashleigh Tuite
We develop a proportional incidence model that estimates vaccine effectiveness (VE) at the population level using conditional likelihood for aggregated data. Our model assumes that the population counts of clinical outcomes for an infectious disease arise from a superposition of Poisson processes with different vaccination statuses. The intensity function in the model is calculated as the product of
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Bayesian causal inference for observational studies with missingness in covariates and outcomes Biometrics (IF 1.9) Pub Date : 2023-08-08 Huaiyu Zang, Hang J. Kim, Bin Huang, Rhonda Szczesniak
Missing data are a pervasive issue in observational studies using electronic health records or patient registries. It presents unique challenges for statistical inference, especially causal inference. Inappropriately handling missing data in causal inference could potentially bias causal estimation. Besides missing data problems, observational health data structures typically have mixed-type variables
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Optimal test procedures for multiple hypotheses controlling the familywise expected loss Biometrics (IF 1.9) Pub Date : 2023-08-02 Willi Maurer, Frank Bretz, Xiaolei Xun
We consider the problem of testing multiple null hypotheses, where a decision to reject or retain must be made for each one and embedding incorrect decisions into a real-life context may inflict different losses. We argue that traditional methods controlling the Type I error rate may be too restrictive in this situation and that the standard familywise error rate may not be appropriate. Using a decision-theoretic
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Rejoinder to discussions on "Optimal test procedures for multiple hypotheses controlling the familywise expected loss". Biometrics (IF 1.9) Pub Date : 2023-08-02 Willi Maurer,Frank Bretz,Xiaolei Xun
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Efficient algorithms for building representative matched pairs with enhanced generalizability Biometrics (IF 1.9) Pub Date : 2023-08-02 Bo Zhang
Many recent efforts center on assessing the ability of real-world evidence (RWE) generated from non-randomized, observational data to produce results compatible with those from randomized controlled trials (RCTs). One noticeable endeavor is the RCT DUPLICATE initiative. To better reconcile findings from an observational study and an RCT, or two observational studies based on different databases, it
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Nonlinear multilevel joint model for individual lesion kinetics and survival to characterize intra-individual heterogeneity in patients with advanced cancer Biometrics (IF 1.9) Pub Date : 2023-07-27 Marion Kerioui, Maxime Beaulieu, Solène Desmée, Julie Bertrand, François Mercier, Jin Y. Jin, René Bruno, Jérémie Guedj
In advanced cancer patients, tumor burden is calculated using the sum of the longest diameters (SLD) of the target lesions, a measure that lumps all lesions together and ignores intra-patient heterogeneity. Here, we used a rich dataset of 342 metastatic bladder cancer patients treated with a novel immunotherapy agent to develop a Bayesian multilevel joint model that can quantify heterogeneity in lesion
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Discussion of “Optimal test procedures for multiple hypotheses controlling the familywise expected loss” by Willi Maurer, Frank Bretz, and Xiaolei Xun Biometrics (IF 1.9) Pub Date : 2023-07-24 L.M. LaVange, E.M. Alt, J.G. Ibrahim
We provide commentary on the paper by Willi Maurer, Frank Bretz, and Xiaolei Xun entitled, “Optimal test procedures for multiple hypotheses controlling for the familywise expected loss.” The authors provide an excellent discussion of the multiplicity problem in clinical trials and propose a novel approach based on a decision-theoretic framework that incorporates loss functions that can vary across
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A self-censoring model for multivariate nonignorable nonmonotone missing data Biometrics (IF 1.9) Pub Date : 2023-07-24 Yilin Li, Wang Miao, Ilya Shpitser, Eric J. Tchetgen Tchetgen
We introduce an itemwise modeling approach called “self-censoring” for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome can be affected by its own value and associated with missingness indicators of other outcomes, while conditionally independent of the other outcomes. The self-censoring model complements previous graphical approaches for the analysis
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Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions Biometrics (IF 1.9) Pub Date : 2023-07-21 Suvo Chatterjee, Shrabanti Chowdhury, Duchwan Ryu, Sanjib Basu
We consider a Bayesian functional data analysis for observations measured as extremely long sequences. Splitting the sequence into several small windows with manageable lengths, the windows may not be independent especially when they are neighboring each other. We propose to utilize Bayesian smoothing splines to estimate individual functional patterns within each window and to establish transition
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Constructing time-invariant dynamic surveillance rules for optimal monitoring schedules Biometrics (IF 1.9) Pub Date : 2023-07-21 Xinyuan Dong, Yingye Zheng, Daniel W. Lin, Lisa Newcomb, Ying-Qi Zhao
Dynamic surveillance rules (DSRs) are sequential surveillance decision rules informing monitoring schedules in clinical practice, which can adapt over time according to a patient's evolving characteristics. In many clinical applications, it is desirable to identify and implement optimal time-invariant DSRs, where the parameters indexing the decision rules are shared across different decision points
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Discussion on “Optimal test procedures for multiple hypotheses controlling the familywise expected loss” by Willi Maurer, Frank Bretz, and Xiaolei Xun Biometrics (IF 1.9) Pub Date : 2023-07-18 Yoav Benjamini, Ruth Heller, Abba Krieger, Saharon Rosset
We discuss three issues. In the first part, we discuss the criteria emphasized by Maurer, Bretz, and Xun, warning that it modifies the per comparison error rate that does not address the concerns raised by multiple testing. In the second part, we strengthen the optimality results developed in the paper, based on our recent results. In the third part, we highlight the potentially important role that
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Discussion on “Optimal test procedures for multiple hypotheses controlling the familywise expected loss” by Willi Maurer, Frank Bretz, and Xiaolei Xun Biometrics (IF 1.9) Pub Date : 2023-07-17 Werner Brannath
This comment builds on the familywise expected loss (FWEL) framework suggested by Maurer, Bretz, and Xun in 2022. By representing the populationwise error rate (PWER) as FWEL, it is illustrated how the FWEL framework can be extended to clinical trials with multiple and overlapping populations and the PWER can be generalized to more general losses. The comment also addresses the question of how to deal
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Simultaneous selection and inference for varying coefficients with zero regions: a soft-thresholding approach Biometrics (IF 1.9) Pub Date : 2023-07-17 Yuan Yang, Ziyang Pan, Jian Kang, Chad Brummett, Yi Li
Varying coefficient models have been used to explore dynamic effects in many scientific areas, such as in medicine, finance, and epidemiology. As most existing models ignore the existence of zero regions, we propose a new soft-thresholded varying coefficient model, where the coefficient functions are piecewise smooth with zero regions. Our new modeling approach enables us to perform variable selection
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Analyzing clustered continuous response variables with ordinal regression models Biometrics (IF 1.9) Pub Date : 2023-07-17 Yuqi Tian, Bryan E. Shepherd, Chun Li, Donglin Zeng, Jonathan S. Schildcrout
Continuous response data are regularly transformed to meet regression modeling assumptions. However, approaches taken to identify the appropriate transformation can be ad hoc and can increase model uncertainty. Further, the resulting transformations often vary across studies leading to difficulties with synthesizing and interpreting results. When a continuous response variable is measured repeatedly
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Analysis of length-biased and partly interval-censored survival data with mismeasured covariates Biometrics (IF 1.9) Pub Date : 2023-07-17 Li-Pang Chen, Bangxu Qiu
In this paper, we analyze the length-biased and partly interval-censored data, whose challenges primarily come from biased sampling and interfere induced by interval censoring. Unlike existing methods that focus on low-dimensional data and assume the covariates to be precisely measured, sometimes researchers may encounter high-dimensional data subject to measurement error, which are ubiquitous in applications
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Melding wildlife surveys to improve conservation inference Biometrics (IF 1.9) Pub Date : 2023-07-13 Justin J. Van Ee, Christian A. Hagen, David C. Pavlacky Jr., Kent A. Fricke, Matthew D. Koslovsky, Mevin B. Hooten
Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several datasets. Individually, these datasets may be insufficient for guiding management due to low spatio-temporal resolution, biased sampling, or large observational uncertainty. Integrated models provide an approach for assimilating
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Transportability of causal inference under random dynamic treatment regimes for kidney–pancreas transplantation Biometrics (IF 1.9) Pub Date : 2023-07-10 Grace R. Lyden, David M. Vock, Erika S. Helgeson, Erik B. Finger, Arthur J. Matas, Jon J. Snyder
A difficult decision for patients in need of kidney–pancreas transplant is whether to seek a living kidney donor or wait to receive both organs from one deceased donor. The framework of dynamic treatment regimes (DTRs) can inform this choice, but a patient-relevant strategy such as “wait for deceased-donor transplant” is ill-defined because there are multiple versions of treatment (i.e., wait times
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Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model Biometrics (IF 1.9) Pub Date : 2023-07-10 David A. Buch, James E. Johndrow, David B. Dunson
The transmission rate is a central parameter in mathematical models of infectious disease. Its pivotal role in outbreak dynamics makes estimating the current transmission rate and uncovering its dependence on relevant covariates a core challenge in epidemiological research as well as public health policy evaluation. Here, we develop a method for flexibly inferring a time-varying transmission rate parameter
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A semiparametric Cox–Aalen transformation model with censored data Biometrics (IF 1.9) Pub Date : 2023-07-04 Xi Ning, Yinghao Pan, Yanqing Sun, Peter B. Gilbert
We propose a broad class of so-called Cox–Aalen transformation models that incorporate both multiplicative and additive covariate effects on the baseline hazard function within a transformation. The proposed models provide a highly flexible and versatile class of semiparametric models that include the transformation models and the Cox–Aalen model as special cases. Specifically, it extends the transformation
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On interquantile smoothness of censored quantile regression with induced smoothing Biometrics (IF 1.9) Pub Date : 2023-06-29 Zexi Cai, Tony Sit
Quantile regression has emerged as a useful and effective tool in modeling survival data, especially for cases where noises demonstrate heterogeneity. Despite recent advancements, non-smooth components involved in censored quantile regression estimators may often yield numerically unstable results, which, in turn, lead to potentially self-contradicting conclusions. We propose an estimating equation-based
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Bayesian model selection for generalized linear mixed models Biometrics (IF 1.9) Pub Date : 2023-06-27 Shuangshuang Xu, Marco A. R. Ferreira, Erica M. Porter, Christopher T. Franck
We propose a Bayesian model selection approach for generalized linear mixed models (GLMMs). We consider covariance structures for the random effects that are widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. Since the random effects cannot be integrated out of GLMMs analytically, we approximate the integrated likelihood function using a pseudo-likelihood
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Sample size and power determination for multiparameter evaluation in nonlinear regression models with potential stratification Biometrics (IF 1.9) Pub Date : 2023-06-25 Michael J. Martens, Soyoung Kim, Kwang Woo Ahn
Sample size and power determination are crucial design considerations for biomedical studies intending to formally test the effects of key variables on an outcome. Other known prognostic factors may exist, necessitating the use of techniques for covariate adjustment when conducting this evaluation. Moreover, the main interest often includes assessing the impact of more than one variable on an outcome
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Hierarchical nuclear norm penalization for multi-view data integration Biometrics (IF 1.9) Pub Date : 2023-06-22 Sangyoon Yi, Raymond Ka Wai Wong, Irina Gaynanova
The prevalence of data collected on the same set of samples from multiple sources (i.e., multi-view data) has prompted significant development of data integration methods based on low-rank matrix factorizations. These methods decompose signal matrices from each view into the sum of shared and individual structures, which are further used for dimension reduction, exploratory analyses, and quantifying
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Dirichlet process mixture models for the analysis of repeated attempt designs Biometrics (IF 1.9) Pub Date : 2023-06-22 Michael J. Daniels, Minji Lee, Wei Feng
In longitudinal studies, it is not uncommon to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful provides useful information for the purposes of assessing missing data assumptions. This is because measurements from subjects who provide the data after numerous failed attempts may differ from those who provide the measurement after fewer attempts
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Ensuring valid inference for Cox hazard ratios after variable selection Biometrics (IF 1.9) Pub Date : 2023-06-22 Kelly Van Lancker, Oliver Dukes, Stijn Vansteelandt
The problem of how to best select variables for confounding adjustment forms one of the key challenges in the evaluation of exposure effects in observational studies, and has been the subject of vigorous recent activity in causal inference. A major drawback of routine procedures is that there is no finite sample size at which they are guaranteed to deliver exposure effect estimators and associated
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Analysis of dynamic restricted mean survival time based on pseudo-observations Biometrics (IF 1.9) Pub Date : 2023-06-19 Zijing Yang, Chengfeng Zhang, Yawen Hou, Zheng Chen
In clinical follow-up studies with a time-to-event end point, the difference in the restricted mean survival time (RMST) is a suitable substitute for the hazard ratio (HR). However, the RMST only measures the survival of patients over a period of time from the baseline and cannot reflect changes in life expectancy over time. Based on the RMST, we study the conditional restricted mean survival time
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Conditional inference in cis-Mendelian randomization using weak genetic factors Biometrics (IF 1.9) Pub Date : 2023-06-19 Ashish Patel, Dipender Gill, Paul Newcombe, Stephen Burgess
Mendelian randomization (MR) is a widely used method to estimate the causal effect of an exposure on an outcome by using genetic variants as instrumental variables. MR analyses that use variants from only a single genetic region (cis-MR) encoding the protein target of a drug are able to provide supporting evidence for drug target validation. This paper proposes methods for cis-MR inference that use
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Flexible joint modeling of mean and dispersion for the directional tuning of neuronal spike counts Biometrics (IF 1.9) Pub Date : 2023-06-16 María Alonso-Pena, Irène Gijbels, Rosa M. Crujeiras
The study of how the number of spikes in a middle temporal visual area (MT/V5) neuron is tuned to the direction of a visual stimulus has attracted considerable attention over the years, but recent studies suggest that the variability of the number of spikes might also be influenced by the directional stimulus. This entails that Poisson regression models are not adequate for this type of data, as the
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Prior and posterior checking of implicit causal assumptions Biometrics (IF 1.9) Pub Date : 2023-06-16 Antonio R. Linero
Causal inference practitioners have increasingly adopted machine learning techniques with the aim of producing principled uncertainty quantification for causal effects while minimizing the risk of model misspecification. Bayesian nonparametric approaches have attracted attention as well, both for their flexibility and their promise of providing natural uncertainty quantification. Priors on high-dimensional
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Dynamic enrichment of Bayesian small-sample, sequential, multiple assignment randomized trial design using natural history data: a case study from Duchenne muscular dystrophy Biometrics (IF 1.9) Pub Date : 2023-06-15 Sidi Wang, Kelley M. Kidwell, Satrajit Roychoudhury
In Duchenne muscular dystrophy (DMD) and other rare diseases, recruiting patients into clinical trials is challenging. Additionally, assigning patients to long-term, multi-year placebo arms raises ethical and trial retention concerns. This poses a significant challenge to the traditional sequential drug development paradigm. In this paper, we propose a small-sample, sequential, multiple assignment
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A double-robust test for high-dimensional gene coexpression networks conditioning on clinical information Biometrics (IF 1.9) Pub Date : 2023-06-13 Maomao Ding, Ruosha Li, Jin Qin, Jing Ning
It has been increasingly appealing to evaluate whether expression levels of two genes in a gene coexpression network are still dependent given samples' clinical information, in which the conditional independence test plays an essential role. For enhanced robustness regarding model assumptions, we propose a class of double-robust tests for evaluating the dependence of bivariate outcomes after controlling
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A stochastic block Ising model for multi-layer networks with inter-layer dependence Biometrics (IF 1.9) Pub Date : 2023-06-07 Jingnan Zhang, Chengye Li, Junhui Wang
Community detection has attracted tremendous interests in network analysis, which aims at finding group of nodes with similar characteristics. Various detection methods have been developed to detect homogeneous communities in multi-layer networks, where inter-layer dependence is a widely acknowledged but severely under-investigated issue. In this paper, we propose a novel stochastic block Ising model
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Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes Biometrics (IF 1.9) Pub Date : 2023-05-24 Jiachen Cai, Ning Zhang, Xin Zhou, Donna Spiegelman, Molin Wang
Epidemiologists are often interested in estimating the effect of functions of time-varying exposure histories in relation to continuous outcomes, for example, cognitive function. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually mismeasured. To obtain unbiased estimates of the effects for mismeasured functions
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Instability of inverse probability weighting methods and a remedy for nonignorable missing data Biometrics (IF 1.9) Pub Date : 2023-05-23 Pengfei Li, Jing Qin, Yukun Liu
Inverse probability weighting (IPW) methods are commonly used to analyze nonignorable missing data (NIMD) under the assumption of a logistic model for the missingness probability. However, solving IPW equations numerically may involve nonconvergence problems when the sample size is moderate and the missingness probability is high. Moreover, those equations often have multiple roots, and identifying
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Group variable selection for the Cox model with interval-censored failure time data Biometrics (IF 1.9) Pub Date : 2023-05-21 Yuxiang Wu, Hui Zhao, Jianguo Sun
Group variable selection is often required in many areas, and for this many methods have been developed under various situations. Unlike the individual variable selection, the group variable selection can select the variables in groups, and it is more efficient to identify both important and unimportant variables or factors by taking into account the existing group structure. In this paper, we consider
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A case study of glucose levels during sleep using multilevel fast function on scalar regression inference Biometrics (IF 1.9) Pub Date : 2023-05-15 Renat Sergazinov, Andrew Leroux, Erjia Cui, Ciprian Crainiceanu, R. Nisha Aurora, Naresh M. Punjabi, Irina Gaynanova
Continuous glucose monitors (CGMs) are increasingly used to measure blood glucose levels and provide information about the treatment and management of diabetes. Our motivating study contains CGM data during sleep for 174 study participants with type II diabetes mellitus measured at a 5-min frequency for an average of 10 nights. We aim to quantify the effects of diabetes medications and sleep apnea
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An accelerated failure time regression model for illness–death data: A frailty approach Biometrics (IF 1.9) Pub Date : 2023-05-17 Lea Kats, Malka Gorfine
This work presents a new model and estimation procedure for the illness–death survival data where the hazard functions follow accelerated failure time (AFT) models. A shared frailty variate induces positive dependence among failure times of a subject for handling the unobserved dependency between the nonterminal and the terminal failure times given the observed covariates. The motivation behind the
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Pathological imaging-assisted cancer gene–environment interaction analysis Biometrics (IF 1.9) Pub Date : 2023-05-03 Kuangnan Fang, Jingmao Li, Qingzhao Zhang, Yaqing Xu, Shuangge Ma
Gene–environment (G–E) interactions have important implications for cancer outcomes and phenotypes beyond the main G and E effects. Compared to main-effect-only analysis, G–E interaction analysis more seriously suffers from a lack of information caused by higher dimensionality, weaker signals, and other factors. It is also uniquely challenged by the “main effects, interactions” variable selection hierarchy
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Imputation-based Q-learning for optimizing dynamic treatment regimes with right-censored survival outcome Biometrics (IF 1.9) Pub Date : 2023-05-02 Lingyun Lyu, Yu Cheng, Abdus S. Wahed
Q-learning has been one of the most commonly used methods for optimizing dynamic treatment regimes (DTRs) in multistage decision-making. Right-censored survival outcome poses a significant challenge to Q-Learning due to its reliance on parametric models for counterfactual estimation which are subject to misspecification and sensitive to missing covariates. In this paper, we propose an imputation-based
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Bi-level structured functional analysis for genome-wide association studies Biometrics (IF 1.9) Pub Date : 2023-04-26 Mengyun Wu, Fan Wang, Yeheng Ge, Shuangge Ma, Yang Li
Genome-wide association studies (GWAS) have led to great successes in identifying genotype–phenotype associations for complex human diseases. In such studies, the high dimensionality of single nucleotide polymorphisms (SNPs) often makes analysis difficult. Functional analysis, which interprets SNPs densely distributed in a chromosomal region as a continuous process rather than discrete observations
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Sparse estimation in semiparametric finite mixture of varying coefficient regression models Biometrics (IF 1.9) Pub Date : 2023-04-17 Abbas Khalili, Farhad Shokoohi, Masoud Asgharian, Shili Lin
Finite mixture of regressions (FMR) are commonly used to model heterogeneous effects of covariates on a response variable in settings where there are unknown underlying subpopulations. FMRs, however, cannot accommodate situations where covariates' effects also vary according to an “index” variable—known as finite mixture of varying coefficient regression (FM-VCR). Although complex, this situation occurs
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Asynchronous and error-prone longitudinal data analysis via functional calibration Biometrics (IF 1.9) Pub Date : 2023-04-12 Xinyue Chang, Yehua Li, Yi Li
In many longitudinal settings, time-varying covariates may not be measured at the same time as responses and are often prone to measurement error. Naive last-observation-carried-forward methods incur estimation biases, and existing kernel-based methods suffer from slow convergence rates and large variations. To address these challenges, we propose a new functional calibration approach to efficiently
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Sparse Bayesian modeling of hierarchical independent component analysis: Reliable estimation of individual differences in brain networks Biometrics (IF 1.9) Pub Date : 2023-04-10 Joshua Lukemire, Giuseppe Pagnoni, Ying Guo
Independent component analysis (ICA) is one of the leading approaches for studying brain functional networks. There is increasing interest in neuroscience studies to investigate individual differences in brain networks and their association with demographic characteristics and clinical outcomes. In this work, we develop a sparse Bayesian group hierarchical ICA model that offers significant improvements
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Conditional cross-design synthesis estimators for generalizability in Medicaid Biometrics (IF 1.9) Pub Date : 2023-04-05 Irina Degtiar, Tim Layton, Jacob Wallace, Sherri Rose
While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of interest. However, few generalizability approaches exist for estimating causal quantities in a target population that is not well-represented by a randomized study but is reflected when additionally incorporating
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Combining mixed effects hidden Markov models with latent alternating recurrent event processes to model diurnal active–rest cycles Biometrics (IF 1.9) Pub Date : 2023-04-05 Benny Ren, Ian Barnett
Data collected from wearable devices can shed light on an individual's pattern of behavioral and circadian routine. Phone use can be modeled as alternating processes, between the state of active use and the state of being idle. Markov chains and alternating recurrent event models are commonly used to model state transitions in cases such as these, and the incorporation of random effects can be used
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Estimating optimal individualized treatment rules with multistate processes Biometrics (IF 1.9) Pub Date : 2023-04-04 Giorgos Bakoyannis
Multistate process data are common in studies of chronic diseases such as cancer. These data are ideal for precision medicine purposes as they can be leveraged to improve more refined health outcomes, compared to standard survival outcomes, as well as incorporate patient preferences regarding quantity versus quality of life. However, there are currently no methods for the estimation of optimal individualized
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Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted maximum likelihood estimation Biometrics (IF 1.9) Pub Date : 2023-03-29 Helene C. W. Rytgaard, Frank Eriksson, Mark J. van der Laan
This work considers targeted maximum likelihood estimation (TMLE) of treatment effects on absolute risk and survival probabilities in classical time-to-event settings characterized by right-censoring and competing risks. TMLE is a general methodology combining flexible ensemble learning and semiparametric efficiency theory in a two-step procedure for substitution estimation of causal parameters. We
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Identifying and estimating effects of sustained interventions under parallel trends assumptions Biometrics (IF 1.9) Pub Date : 2023-03-29 Audrey Renson, Michael G. Hudgens, Alexander P. Keil, Paul N. Zivich, Allison E. Aiello
Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions typically require a measured covariate set sufficient to control confounding, which can be questionable in observational studies. Differences-in-differences rely instead on the parallel trends assumption, allowing for some
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Detecting the spatial clustering of exposure–response relationships with estimation error: a novel spatial scan statistic Biometrics (IF 1.9) Pub Date : 2023-03-25 Wei Wang, Sheng Li, Tao Zhang, Fei Yin, Yue Ma
Detecting the spatial clustering of the exposure–response relationship (ERR) between environmental risk factors and health-related outcomes plays important roles in disease control and prevention, such as identifying highly sensitive regions, exploring the causes of heterogeneous ERRs, and designing region-specific health intervention measures. However, few studies have focused on this issue. A possible