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Content
December 2023, Volume 79, Issue 4
- 2781-2793 Optimal test procedures for multiple hypotheses controlling the familywise expected loss
by Willi Maurer & Frank Bretz & Xiaolei Xun
- 2794-2797 Discussion on “Optimal test procedures for multiple hypotheses controlling the familywise expected loss” by Willi Maurer, Frank Bretz, and Xiaolei Xun
by Yoav Benjamini & Ruth Heller & Abba Krieger & Saharon Rosset
- 2798-2801 Discussion of “Optimal test procedures for multiple hypotheses controlling the familywise expected loss” by Willi Maurer, Frank Bretz, and Xiaolei Xun
by Sudipto Banerjee
- 2802-2805 Discussion of “Optimal test procedures for multiple hypotheses controlling the familywise expected loss” by Willi Maurer, Frank Bretz, and Xiaolei Xun
by L.M. LaVange & E.M. Alt & J.G. Ibrahim
- 2806-2810 Discussion on “Optimal test procedures for multiple hypotheses controlling the familywise expected loss” by Willi Maurer, Frank Bretz, and Xiaolei Xun
by Werner Brannath
- 2811-2814 Rejoinder to discussions on “Optimal test procedures for multiple hypotheses controlling the familywise expected loss”
by Willi Maurer & Frank Bretz & Xiaolei Xun
- 2815-2829 Optimizing treatment allocation in randomized clinical trials by leveraging baseline covariates
by Wei Zhang & Zhiwei Zhang & Aiyi Liu
- 2830-2842 Estimating optimal individualized treatment rules with multistate processes
by Giorgos Bakoyannis
- 2843-2856 Stabilized direct learning for efficient estimation of individualized treatment rules
by Kushal S. Shah & Haoda Fu & Michael R. Kosorok
- 2857-2868 SAM: Self‐adapting mixture prior to dynamically borrow information from historical data in clinical trials
by Peng Yang & Yuansong Zhao & Lei Nie & Jonathon Vallejo & Ying Yuan
- 2869-2880 Regression‐based multiple treatment effect estimation under covariate‐adaptive randomization
by Yujia Gu & Hanzhong Liu & Wei Ma
- 2881-2894 Interim monitoring of sequential multiple assignment randomized trials using partial information
by Cole Manschot & Eric Laber & Marie Davidian
- 2895-2906 Covariate‐adjusted response‐adaptive designs based on semiparametric approaches
by Hai Zhu & Hongjian Zhu
- 2907-2919 DROID: dose‐ranging approach to optimizing dose in oncology drug development
by Beibei Guo & Ying Yuan
- 2920-2932 Relative contrast estimation and inference for treatment recommendation
by Muxuan Liang & Menggang Yu
- 2933-2946 Hierarchical nuclear norm penalization for multi‐view data integration
by Sangyoon Yi & Raymond Ka Wai Wong & Irina Gaynanova
- 2947-2960 An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis
by Chixiang Chen & Ming Wang & Shuo Chen
- 2961-2973 Combining observational and experimental datasets using shrinkage estimators
by Evan T.R. Rosenman & Guillaume Basse & Art B. Owen & Mike Baiocchi
- 2974-2986 Optimal sampling for positive only electronic health record data
by Seong‐H. Lee & Yanyuan Ma & Ying Wei & Jinbo Chen
- 2987-2997 Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model
by David A. Buch & James E. Johndrow & David B. Dunson
- 2998-3009 Identifying and estimating effects of sustained interventions under parallel trends assumptions
by Audrey Renson & Michael G. Hudgens & Alexander P. Keil & Paul N. Zivich & Allison E. Aiello
- 3010-3022 Additive subdistribution hazards regression for competing risks data in case‐cohort studies
by Adane F. Wogu & Haolin Li & Shanshan Zhao & Hazel B. Nichols & Jianwen Cai
- 3023-3037 Nonparametric failure time: Time‐to‐event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures
by Rodney A. Sparapani & Brent R. Logan & Martin J. Maiers & Purushottam W. Laud & Robert E. McCulloch
- 3038-3049 Estimation of time‐specific intervention effects on continuously distributed time‐to‐event outcomes by targeted maximum likelihood estimation
by Helene C. W. Rytgaard & Frank Eriksson & Mark J. van der Laan
- 3050-3065 Information criteria for detecting change‐points in the Cox proportional hazards model
by Ryoto Ozaki & Yoshiyuki Ninomiya
- 3066-3081 An accelerated failure time regression model for illness–death data: A frailty approach
by Lea Kats & Malka Gorfine
- 3082-3095 Group variable selection for the Cox model with interval‐censored failure time data
by Yuxiang Wu & Hui Zhao & Jianguo Sun
- 3096-3110 Ensuring valid inference for Cox hazard ratios after variable selection
by Kelly Van Lancker & Oliver Dukes & Stijn Vansteelandt
- 3111-3125 A semiparametric Cox–Aalen transformation model with censored data
by Xi Ning & Yinghao Pan & Yanqing Sun & Peter B. Gilbert
- 3126-3139 Efficient and flexible estimation of natural direct and indirect effects under intermediate confounding and monotonicity constraints
by Kara E. Rudolph & Nicholas Williams & Iván Díaz
- 3140-3152 Improved inference for doubly robust estimators of heterogeneous treatment effects
by Heejun Shin & Joseph Antonelli
- 3153-3164 Prior and posterior checking of implicit causal assumptions
by Antonio R. Linero
- 3165-3178 Transportability of causal inference under random dynamic treatment regimes for kidney–pancreas transplantation
by Grace R. Lyden & David M. Vock & Erika S. Helgeson & Erik B. Finger & Arthur J. Matas & Jon J. Snyder
- 3179-3190 Entropy balancing for causal generalization with target sample summary information
by Rui Chen & Guanhua Chen & Menggang Yu
- 3191-3202 Individualized causal discovery with latent trajectory embedded Bayesian networks
by Fangting Zhou & Kejun He & Yang Ni
- 3203-3214 A self‐censoring model for multivariate nonignorable nonmonotone missing data
by Yilin Li & Wang Miao & Ilya Shpitser & Eric J. Tchetgen Tchetgen
- 3215-3226 Instability of inverse probability weighting methods and a remedy for nonignorable missing data
by Pengfei Li & Jing Qin & Yukun Liu
- 3227-3238 A double‐robust test for high‐dimensional gene coexpression networks conditioning on clinical information
by Maomao Ding & Ruosha Li & Jin Qin & Jing Ning
- 3239-3251 A Bayesian zero‐inflated Dirichlet‐multinomial regression model for multivariate compositional count data
by Matthew D. Koslovsky
- 3252-3265 Bayesian nonparametric adjustment of confounding
by Chanmin Kim & Mauricio Tec & Corwin Zigler
- 3266-3278 Bayesian model selection for generalized linear mixed models
by Shuangshuang Xu & Marco A. R. Ferreira & Erica M. Porter & Christopher T. Franck
- 3279-3293 Functional Bayesian networks for discovering causality from multivariate functional data
by Fangting Zhou & Kejun He & Kunbo Wang & Yanxun Xu & Yang Ni
- 3294-3306 Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions
by Suvo Chatterjee & Shrabanti Chowdhury & Duchwan Ryu & Sanjib Basu
- 3307-3318 Latent factor model for multivariate functional data
by Ruonan Li & Luo Xiao
- 3319-3331 Nonlinear function‐on‐scalar regression via functional universal approximation
by Ruiyan Luo & Xin Qi
- 3332-3344 Homogeneity tests of covariance for high‐dimensional functional data with applications to event segmentation
by Ping‐Shou Zhong
- 3345-3358 Latent deformation models for multivariate functional data and time‐warping separability
by Cody Carroll & Hans‐Georg Müller
- 3359-3373 Bi‐level structured functional analysis for genome‐wide association studies
by Mengyun Wu & Fan Wang & Yeheng Ge & Shuangge Ma & Yang Li
- 3374-3387 Asynchronous and error‐prone longitudinal data analysis via functional calibration
by Xinyue Chang & Yehua Li & Yi Li
- 3388-3401 Simultaneous selection and inference for varying coefficients with zero regions: a soft‐thresholding approach
by Yuan Yang & Ziyang Pan & Jian Kang & Chad Brummett & Yi Li
- 3402-3417 Combining mixed effects hidden Markov models with latent alternating recurrent event processes to model diurnal active–rest cycles
by Benny Ren & Ian Barnett
- 3418-3430 Longitudinal incremental propensity score interventions for limited resource settings
by Aaron L. Sarvet & Kerollos N. Wanis & Jessica G. Young & Roberto Hernandez‐Alejandro & Mats J. Stensrud
- 3431-3444 Flexible joint modeling of mean and dispersion for the directional tuning of neuronal spike counts
by María Alonso‐Pena & Irène Gijbels & Rosa M. Crujeiras
- 3445-3457 Sparse estimation in semiparametric finite mixture of varying coefficient regression models
by Abbas Khalili & Farhad Shokoohi & Masoud Asgharian & Shili Lin
- 3458-3471 Conditional inference in cis‐Mendelian randomization using weak genetic factors
by Ashish Patel & Dipender Gill & Paul Newcombe & Stephen Burgess
- 3472-3484 Competition‐based control of the false discovery proportion
by Dong Luo & Arya Ebadi & Kristen Emery & Yilun He & William Stafford Noble & Uri Keich
- 3485-3496 Multiresolution categorical regression for interpretable cell‐type annotation
by Aaron J. Molstad & Keshav Motwani
- 3497-3509 False discovery rate‐controlled multiple testing for union null hypotheses: a knockoff‐based approach
by Ran Dai & Cheng Zheng
- 3510-3521 Analyzing data in complicated 3D domains: Smoothing, semiparametric regression, and functional principal component analysis
by Eleonora Arnone & Luca Negri & Ferruccio Panzica & Laura M. Sangalli
- 3522-3532 Detecting the spatial clustering of exposure–response relationships with estimation error: a novel spatial scan statistic
by Wei Wang & Sheng Li & Tao Zhang & Fei Yin & Yue Ma
- 3533-3548 A seasonality‐adjusted sequential test for vaccine safety surveillance
by Rex Shen & Keran Moll & Ying Lu & Lu Tian
- 3549-3563 On interquantile smoothness of censored quantile regression with induced smoothing
by Zexi Cai & Tony Sit
- 3564-3573 A stochastic block Ising model for multi‐layer networks with inter‐layer dependence
by Jingnan Zhang & Chengye Li & Junhui Wang
- 3574-3585 Generating designs for comparative experiments with two blocking factors
by Nha Vo‐Thanh & Hans‐Peter Piepho
- 3586-3598 Bayesian design of multi‐regional clinical trials with time‐to‐event endpoints
by Nathan William Bean & Joseph George Ibrahim & Matthew Austin Psioda
- 3599-3611 Sparse Bayesian modeling of hierarchical independent component analysis: Reliable estimation of individual differences in brain networks
by Joshua Lukemire & Giuseppe Pagnoni & Ying Guo
- 3612-3623 Dynamic enrichment of Bayesian small‐sample, sequential, multiple assignment randomized trial design using natural history data: a case study from Duchenne muscular dystrophy
by Sidi Wang & Kelley M. Kidwell & Satrajit Roychoudhury
- 3624-3636 Bayesian causal inference for observational studies with missingness in covariates and outcomes
by Huaiyu Zang & Hang J. Kim & Bin Huang & Rhonda Szczesniak
- 3637-3649 Spatially adaptive calibrations of airbox PM2.5 data
by ShengLi Tzeng & Chi‐Wei Lai & Hsin‐Cheng Huang
- 3650-3663 Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data
by Joshua L. Warren & Melanie H. Chitwood & Benjamin Sobkowiak & Caroline Colijn & Ted Cohen
- 3664-3675 Latent trajectory models for spatio‐temporal dynamics in Alaskan ecosystems
by Xinyi Lu & Mevin B. Hooten & Ann M. Raiho & David K. Swanson & Carl A. Roland & Sarah E. Stehn
- 3676-3689 Imputation‐based Q‐learning for optimizing dynamic treatment regimes with right‐censored survival outcome
by Lingyun Lyu & Yu Cheng & Abdus S. Wahed
- 3690-3700 Analysis of dynamic restricted mean survival time based on pseudo‐observations
by Zijing Yang & Chengfeng Zhang & Yawen Hou & Zheng Chen
- 3701-3714 Study design for restricted mean time analysis of recurrent events and death
by Lu Mao
- 3715-3727 Finding influential subjects in a network using a causal framework
by Youjin Lee & Ashley L. Buchanan & Elizabeth L. Ogburn & Samuel R. Friedman & M. Elizabeth Halloran & Natallia V. Katenka & Jing Wu & Georgios K. Nikolopoulos
- 3728-3738 Causal mediation analysis using high‐dimensional image mediator bounded in irregular domain with an application to breast cancer
by Shu Jiang & Graham A. Colditz
- 3739-3751 Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes
by Jiachen Cai & Ning Zhang & Xin Zhou & Donna Spiegelman & Molin Wang
- 3752-3763 Nonlinear multilevel joint model for individual lesion kinetics and survival to characterize intra‐individual heterogeneity in patients with advanced cancer
by Marion Kerioui & Maxime Beaulieu & Solène Desmée & Julie Bertrand & François Mercier & Jin Y. Jin & René Bruno & Jérémie Guedj
- 3764-3777 Analyzing clustered continuous response variables with ordinal regression models
by Yuqi Tian & Bryan E. Shepherd & Chun Li & Donglin Zeng & Jonathan S. Schildcrout
- 3778-3791 A nonparametric test of group distributional differences for hierarchically clustered functional data
by Alexander S. Long & Brian J. Reich & Ana‐Maria Staicu & John Meitzen
- 3792-3802 Bayesian inference for a principal stratum estimand on recurrent events truncated by death
by Tianmeng Lyu & Björn Bornkamp & Guenther Mueller‐Velten & Heinz Schmidli
- 3803-3817 Estimating population size: The importance of model and estimator choice
by Matthew R. Schofield & Richard J. Barker & William A. Link & Heloise Pavanato
- 3818-3830 Modeling COVID‐19 contact‐tracing using the ratio regression capture–recapture approach
by Dankmar Böhning & Rattana Lerdsuwansri & Patarawan Sangnawakij
- 3831-3845 A synthetic data integration framework to leverage external summary‐level information from heterogeneous populations
by Tian Gu & Jeremy Michael George Taylor & Bhramar Mukherjee
- 3846-3858 Supervised convex clustering
by Minjie Wang & Tianyi Yao & Genevera I. Allen
- 3859-3872 Conditional cross‐design synthesis estimators for generalizability in Medicaid
by Irina Degtiar & Tim Layton & Jacob Wallace & Sherri Rose
- 3873-3882 A case study of glucose levels during sleep using multilevel fast function on scalar regression inference
by Renat Sergazinov & Andrew Leroux & Erjia Cui & Ciprian Crainiceanu & R. Nisha Aurora & Naresh M. Punjabi & Irina Gaynanova
- 3883-3894 Pathological imaging‐assisted cancer gene–environment interaction analysis
by Kuangnan Fang & Jingmao Li & Qingzhao Zhang & Yaqing Xu & Shuangge Ma
- 3895-3906 Constructing time‐invariant dynamic surveillance rules for optimal monitoring schedules
by Xinyuan Dong & Yingye Zheng & Daniel W. Lin & Lisa Newcomb & Ying‐Qi Zhao
- 3907-3915 Dirichlet process mixture models for the analysis of repeated attempt designs
by Michael J. Daniels & Minji Lee & Wei Feng
- 3916-3928 Sample size and power determination for multiparameter evaluation in nonlinear regression models with potential stratification
by Michael J. Martens & Soyoung Kim & Kwang Woo Ahn
- 3929-3940 Analysis of length‐biased and partly interval‐censored survival data with mismeasured covariates
by Li‐Pang Chen & Bangxu Qiu
- 3941-3953 Melding wildlife surveys to improve conservation inference
by Justin J. Van Ee & Christian A. Hagen & David C. Pavlacky Jr. & Kent A. Fricke & Matthew D. Koslovsky & Mevin B. Hooten
- 3954-3967 A proportional incidence rate model for aggregated data to study the vaccine effectiveness against COVID‐19 hospital and ICU admissions
by Ping Yan & Muhammad Abu Shadeque Mullah & Ashleigh Tuite
- 3968-3980 A second evidence factor for a second control group
by Paul R. Rosenbaum
- 3981-3997 Efficient algorithms for building representative matched pairs with enhanced generalizability
by Bo Zhang
- 3998-4011 How to analyze continuous and discrete repeated measures in small‐sample cross‐over trials?
by Johan Verbeeck & Martin Geroldinger & Konstantin Thiel & Andrew Craig Hooker & Sebastian Ueckert & Mats Karlsson & Arne Cornelius Bathke & Johann Wolfgang Bauer & Geert Molenberghs & Georg Zimmermann
- 4013-4013 Statistical inference and machine learning for big data by Mayer Alvo, Springer Cham. 2022. pp. 431. EUR 129.99. ISBN‐13: 978‐3‐031‐06783‐9
by Li‐Pang Chen
- 4014-4016 Bioinformatics methods: From omics to next generation sequencing By Shili Lin, Denise Scholtens, Sujay Datta, Boca Raton, FL: Chapman & Hall. 2023. pp. 350. ISBN 9781498765152
by Yu‐Chiao Chiu
September 2023, Volume 79, Issue 3
June 2023, Volume 79, Issue 2
- 539-550 Instrumental variable estimation of the causal hazard ratio
by Linbo Wang & Eric Tchetgen Tchetgen & Torben Martinussen & Stijn Vansteelandt
- 551-553 Discussion on “Instrumental variable estimation of the causal hazard ratio,” by Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, Stijn Vansteelandt
by Brigham Russell Frandsen
- 554-558 Discussion on “Instrumental variable estimation of the causal hazard ratio,” by Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, and Stijn Vansteelandt
by Benjamin R. Baer & Robert L. Strawderman & Ashkan Ertefaie
- 559-563 Discussion on “Instrumental variable estimation of the causal hazard ratio” by Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, and Stijn Vansteelandt
by A. James O'Malley & Pablo Martínez‐Camblor & Todd A. MacKenzie
- 564-568 Rejoinder to discussions on “Instrumental variable estimation of the causal hazard ratio”
by Linbo Wang & Eric Tchetgen Tchetgen & Torben Martinussen & Stijn Vansteelandt
- 569-581 Instrumented difference‐in‐differences
by Ting Ye & Ashkan Ertefaie & James Flory & Sean Hennessy & Dylan S. Small
- 582-586 Discussion on “Instrumented difference‐in‐differences” by Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy, and Dylan S. Small
by Jad Beyhum & Jean‐Pierre Florens & Ingrid Van Keilegom
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