Cover Image

Causal Covariate Selection for the Imputation-based Regression Calibration Method for Exposure Measurement Error Bias Correction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tang, Wenze, Spiegelman, Donna, Wu, Yujie, Wang, Molin
Format:	Preprint
Published:	2023
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Causal Covariate Selection for the Regression Calibration Method for Exposure Measurement Error Bias Correction
by: Wenze Tang, et al.
Published: (2026)

Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure
by: Tang, Wenze, et al.
Published: (2022)

Transportability of Regression Calibration with External Validation Studies for Measurement Error Correction
by: Li, Zexiang, et al.
Published: (2026)

Measurement Error Correction for Spatially Defined Environmental Exposures in Survival Analysis
by: Ge, Lin, et al.
Published: (2024)

Improving Transportability of Regression Calibration Under the Main/External Validation Study Design
by: Li, Zexiang, et al.
Published: (2025)

A Penalized Functional Linear Cox Regression Model for Spatially-defined Environmental Exposure with an Estimated Buffer Distance
by: Lee, Jooyoung, et al.
Published: (2023)

Exposure measurement error correction in longitudinal studies with discrete outcomes
by: Yang, Ce, et al.
Published: (2025)

Statistical methods for cost-effectiveness analysis of left-truncated censored survival data with treatment delays
by: Khudyakov, Polyna, et al.
Published: (2025)

MECfda: An R Package for Bias Correction Due to Measurement Error in Functional and Scalar Covariates in Scalar-on-Function Regression Models
by: Ji, Heyang, et al.
Published: (2025)

Correcting for bias due to mismeasured exposure in mediation analysis with a survival outcome
by: Cheng, Chao, et al.
Published: (2023)

Correcting Measurement Error and Zero Inflation in Functional Covariates for Scalar-on-Function Quantile Regression
by: Qin, Caihong, et al.
Published: (2026)

Statistical methods for clustered competing risk data when the event types are only available in a training dataset
by: Wu, Yujie, et al.
Published: (2025)

Sample Size and Bias Approximations For Continuous Exposures Measured with Error
by: Kim, Honghyok
Published: (2024)

Calibrated Bayes analysis of cluster-randomized trials
by: Liu, Ruyi, et al.
Published: (2025)

Correcting Conditional Mean Imputation for Censored Covariates and Improving Usability
by: Lotspeich, Sarah C., et al.
Published: (2021)

Design of egocentric network-based studies to estimate causal effects under interference
by: Fang, Junhan, et al.
Published: (2023)

Variable Selection for Linear Regression Imputation in Surveys
by: An, Ziming, et al.
Published: (2026)

Exposure Measurement Error Correction in Longitudinal Studies With Discrete Outcomes
by: Ce Yang, et al.
Published: (2025)

Learn-As-you-GO (LAGO) Trials: Optimizing Trials for Effectiveness and Power to Prevent Failed Trials
by: Bing, Ante, et al.
Published: (2025)

Identifying Key Influencers using an Egocentric Network-based Randomized Design
by: He, Zhibing, et al.
Published: (2025)

Addressing Confounding and Continuous Exposure Measurement Error Using Corrected Score Functions
by: Richardson, Brian D., et al.
Published: (2024)

Flexible and Efficient Estimation of Causal Effects with Error-Prone Exposures: A Control Variates Approach for Measurement Error
by: Barnatchez, Keith, et al.
Published: (2024)

Improving Causal Inference with Measurement Errors in Exposures and Confounders: A New Method and Its Application to Air Pollution Exposure Assessment and Epidemiology
by: Kim, Honghyok
Published: (2024)

Sequential Hierarchical Regression Imputation with Variable Selection Routines
by: Li, Qiushuang, et al.
Published: (2025)

Parametric Modal Regression with Error in Covariates
by: Liu, Qingyang, et al.
Published: (2022)

Bias Correction for Semiparametric Regression Models
by: Zhang, Yuming, et al.
Published: (2026)

Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding
by: Hafer, Marlies, et al.
Published: (2025)

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints
by: Owen, Melody, et al.
Published: (2025)

Causal Effect Identification and Inference with Endogenous Exposures and a Light-tailed Error
by: Wang, Ruoyu, et al.
Published: (2024)

Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment
by: Heng, Siyu, et al.
Published: (2023)

Covariance Regression based on Basis Expansion
by: Bak, Kwan-Young, et al.
Published: (2025)

Measures for Assessing Causal Effect Heterogeneity Unexplained by Covariates
by: Kawakami, Yuta, et al.
Published: (2026)

Nonparametric Regression and Error Covariance Function Estimation -- Beyond Short-Range Dependence
by: Liu, Sisheng, et al.
Published: (2025)

Correcting the Coverage Bias of Quantile Regression
by: Gibbs, Isaac, et al.
Published: (2025)

Deep Regression for Repeated Measurements under Covariate Shift
by: Wang, Yingxuan, et al.
Published: (2026)

Nonlinear Regression with Residuals: Causal Estimation with Time-varying Treatments and Covariates
by: Bates, Stephen, et al.
Published: (2022)

Robust Variable Selection for High-dimensional Regression with Missing Data and Measurement Errors
by: Zhang, Zhenhao, et al.
Published: (2024)

Model Selection for Causal Modeling in Missing Exposure Problems
by: Shi, Yuliang, et al.
Published: (2024)

Temporal Wasserstein Imputation: A Versatile Method for Time Series Imputation
by: Huang, Shuo-Chieh, et al.
Published: (2024)

Clustered Covariate Regression
by: Soale, Abdul-Nasah, et al.
Published: (2023)