Cover Image

Time-lag bias induced by unobserved heterogeneity: comparing treated patients to controls with a different start of follow-up

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	van Eekelen, Rik, Bossuyt, Patrick M. M., van Geloven, Nan
Format:	Preprint
Published:	2021
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Prediction under interventions: evaluation of counterfactual performance using longitudinal observational data
by: Keogh, Ruth H., et al.
Published: (2023)

Doubly robust estimation of marginal cumulative incidence curves for competing risk analysis
by: van Hage, Patrick, et al.
Published: (2024)

The super learner for time-to-event outcomes: A tutorial
by: Keogh, Ruth H., et al.
Published: (2025)

When accurate prediction models yield harmful self-fulfilling prophecies
by: van Amsterdam, Wouter A. C., et al.
Published: (2023)

Penalized mixed models to adjust for batch effects and unobserved confounding in high dimensional regression
by: Lu, Yujing, et al.
Published: (2025)

A mixture of distributed lag non-linear models to account for spatially heterogeneous exposure-lag-response associations
by: Briz-Redón, Álvaro, et al.
Published: (2025)

Graphical tools for detection and control of selection bias with multiple exposures and samples
by: Schnell, Patrick M., et al.
Published: (2024)

Prediction meets causal inference: the role of treatment in clinical prediction models
by: van Geloven, Nan, et al.
Published: (2020)

Analysis of spatially clustered survival data with unobserved covariates using SBART
by: Ghosh, Durbadal, et al.
Published: (2024)

The hierarchical barycenter: conditional probability simulation with structured and unobserved covariates
by: Tabak, Esteban G., et al.
Published: (2025)

Risk-based decision making: estimands for sequential prediction under interventions
by: Luijken, Kim, et al.
Published: (2023)

Irregular measurement times in estimating time-varying treatment effects: Categorizing biases and comparing adjustment methods
by: Kant, Wouter M. R., et al.
Published: (2025)

Bayesian inference for functional extreme events defined via partially unobserved processes
by: Thannheimer, Max, et al.
Published: (2025)

The risks of risk assessment: causal blind spots when using prediction models for treatment decisions
by: van Geloven, Nan, et al.
Published: (2024)

Fusion of Tree-induced Regressions for Clinico-genomic Data
by: Goedhart, Jeroen M., et al.
Published: (2024)

Correcting inferences for volunteer-collected data with geospatial sampling bias
by: Lugtig, Peter, et al.
Published: (2022)

Dose optimization design accounting for unknown patient heterogeneity in cancer clinical trials
by: Silva, Rebecca B., et al.
Published: (2025)

Testing for sufficient follow-up in survival data with a cure fraction
by: Yuen, Tsz Pang, et al.
Published: (2024)

Testing for sufficient follow-up in cure models with categorical covariates
by: Yuen, Tsz Pang, et al.
Published: (2025)

Smooth and shape-constrained quantile distributed lag models
by: Jin, Yisen, et al.
Published: (2024)

Strategy to control biases in prior event rate ratio method, with application to palliative care in patients with advanced cancer
by: Ma, Xiangmei, et al.
Published: (2024)

Efficient estimation and correction of selection-induced bias with order statistics
by: McLatchie, Yann, et al.
Published: (2023)

Bayesian subcohort selection for longitudinal covariate measurements in follow-up studies
by: Reinikainen, Jaakko, et al.
Published: (2016)

A unifying modelling approach for hierarchical distributed lag models
by: Economou, Theo, et al.
Published: (2024)

Time-lagged marginal expected shortfall
by: Liu, Jiajun, et al.
Published: (2025)

Treatment effect estimation by comparing observed and predicted outcomes: theory and practical illustration in radiotherapy
by: Meijerink, Lotta M., et al.
Published: (2025)

Redefining shared information: a heterogeneity-adaptive framework for meta-analysis
by: Davis, Elizabeth M., et al.
Published: (2026)

Spatially varying distributed lag non-linear models using Laplacian P-splines
by: Rutten, Sara, et al.
Published: (2026)

Multiple imputation in data that grow over time: A comparison of three strategies
by: Kavelaars, X. M., et al.
Published: (2019)

Distilling heterogeneous treatment effects: Stable subgroup estimation in causal inference
by: Huang, Melody, et al.
Published: (2025)

Valid causal inference with unobserved confounding in high-dimensional settings
by: Moosavi, Niloofar, et al.
Published: (2024)

Distributed lag non-linear models with spatial effect modification using Laplacian P-splines
by: Rutten, Sara, et al.
Published: (2026)

Unsupervised dense random survival forests identify interpretable patient profiles with heterogeneous treatment benefit
by: Li, Xingyu, et al.
Published: (2026)

Leveraging external data in the analysis of randomized controlled trials: a comparative analysis
by: Kotecha, Gopal, et al.
Published: (2024)

Distributed lag non-linear models with Laplacian-P-splines for analysis of spatially structured time series
by: Rutten, Sara, et al.
Published: (2025)

A simple distributional difference-in-differences estimator for univariate and bivariate outcomes
by: Fernández-Val, Iván, et al.
Published: (2024)

Variable selection in spatial lag models using the focussed information criterion
by: Pandhare, Sagar, et al.
Published: (2025)

A comparison of methods for estimating the average treatment effect on the treated for externally controlled trials
by: Wang, Huan, et al.
Published: (2024)

Link prediction in ecological networks under extreme taxonomic bias
by: Kampe, Jennifer N., et al.
Published: (2025)

Improving prediction in M-estimation by integrating external information from heterogeneous populations
by: Dempsey, Walter, et al.
Published: (2025)