Imagen de Portada

Bias in studies of prenatal exposures using real-world data due to pregnancy identification method

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Latour, Chase D., Edwards, Jessie K., Funk, Michele Jonsson, Suarez, Elizabeth A., Boggess, Kim, Wood, Mollie E.
Formato:	Preprint
Publicado:	2025
Materias:	Applications
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Healthy Live Births Should be Considered as Competing Events when Estimating the Total Effect of Prenatal Medication Use on Pregnancy Outcomes
por: Latour, Chase D., et al.
Publicado: (2024)

Healthy Live Births as Censoring Versus Competing Events in Studies of Prenatal Medication Use
por: Chase D. Latour, et al.
Publicado: (2025)

Using Statistical Precision Medicine to Identify Optimal Treatments in a Heart Failure Setting
por: Virkud, Arti, et al.
Publicado: (2025)

Comparing causal estimands from sequential nested versus single point target trials: A simulation study
por: Wiener, Catherine, et al.
Publicado: (2026)

Studying early pregnancy exposures and outcomes in routinely collected healthcare data: Conceiving of target trials
por: Mollie E. Wood
Publicado: (2024)

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

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data
por: Bouvier, Florie, et al.
Publicado: (2023)

Variational Bayes latent class approach for EHR-based phenotyping with large real-world data
por: Buckley, Brian, et al.
Publicado: (2023)

A machine learning approach to study plant functional trait divergence
por: Sambadi Majumder, et al.
Publicado: (2024)

A framework to analyze biodiversity capacity at landscape level and identify areas of high ecological importance
por: Annie Jonsson, et al.
Publicado: (2025)

Demographic Distribution Matching between real world and virtual phantom population
por: Ghosh, Dhrubajyoti, et al.
Publicado: (2025)

Feature aware covariance estimation, with application to mixtures of chemical exposures
por: Bersson, Elizabeth, et al.
Publicado: (2025)

Accounting for Missing Data in Public Health Research Using a Synthesis of Statistical and Mathematical Models
por: Zivich, Paul N, et al.
Publicado: (2025)

Novel g-computation algorithms for time-varying actions with recurrent and semi-competing events
por: D'Alessio, Alena Sorensen, et al.
Publicado: (2026)

Invasion timing affects multiple scales, metrics, and facets of biodiversity outcomes in ecological restoration experiments
por: Emma Ladouceur, et al.
Publicado: (2025)

A Bayesian Approach to GRAPPA Parallel FMRI Image Reconstruction Increases SNR and Power of Task Detection
por: Sakitis, Chase J, et al.
Publicado: (2024)

A method to convert traditional fingerprint ACE / ACE-V outputs ("identification", "inconclusive", "exclusion") to Bayes factors
por: Morrison, Geoffrey Stewart
Publicado: (2024)

Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones
por: Hu, Addison J., et al.
Publicado: (2020)

Deciphering seasonal depression variations and interplays between weather changes, physical activity, and depression severity in real-world settings: Learnings from RADAR-MDD longitudinal mobile health study
por: Zhang, Yuezhou, et al.
Publicado: (2024)

Benthic habitat structure explains broad‐scale patterns in reef fish communities
por: Mollie Asbury, et al.
Publicado: (2026)

Towards more realistic climate model outputs: A multivariate bias correction based on zero-inflated vine copulas
por: Funk, Henri, et al.
Publicado: (2024)

On the two-step hybrid design for augmenting randomized trials using real-world data
por: Xu, Jiapeng, et al.
Publicado: (2025)

Propensity score matching in semaglutide retrospective studies
por: Mohney, Elizabeth, et al.
Publicado: (2025)

Bias correction in treatment effect estimates following data-driven biomarker cutoff selection
por: Zhang, Chi, et al.
Publicado: (2025)

Estimating temporary emigration from capture-recapture data in the presence of latent identification
por: Skopalova, Katarina, et al.
Publicado: (2025)

Analysis of plant metabolomics data using identification‐free approaches
por: Xinyu Yuan, et al.
Publicado: (2025)

Improved order selection method for hidden Markov models: a case study with movement data
por: Dupont, Fanny, et al.
Publicado: (2024)

A Bayesian approach to aggregated chemical exposure assessment
por: Neucker, Sophie Van Den, et al.
Publicado: (2025)

A two-stage approach to heat-mortality risk assessment comparing multiple exposure-to-temperature models: the case study in Lazio, Italy
por: Ceccarelli, Emiliano, et al.
Publicado: (2025)

Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests
por: Kapar, Jan, et al.
Publicado: (2025)

Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation
por: Sambadi Majumder, et al.
Publicado: (2025)

A systematic review of sample size determination in Bayesian randomized clinical trials: full Bayesian methods are rarely used
por: Marks, Yanara, et al.
Publicado: (2025)

Capturing Aleatoric Uncertainty in Climate Models
por: Gruber, Cornelia, et al.
Publicado: (2026)

Bias in Meta-Analytic Modeling of Surrogate Endpoints in Cancer Screening Trials
por: Long, James P., et al.
Publicado: (2025)

Observing the unobserved confounding through its effects: toward randomized trial-like estimates from real-world survival data
por: Stoumpou, Vasiliki, et al.
Publicado: (2026)

Bias reduction method for prior event rate ratio, with application to emergency department visit rates in patients with advanced cancer
por: Ma, Xiangmei, et al.
Publicado: (2025)

BISON: Bi-clustering of spatial omics data with feature selection
por: Zhu, Bencong, et al.
Publicado: (2025)

A survey of using EHR as real-world evidence for discovering and validating new drug indications
por: Talukdar, Nabasmita, et al.
Publicado: (2025)

Double Robust Variance Estimation with Parametric Working Models
por: Shook-Sa, Bonnie E., et al.
Publicado: (2024)

An open-source framework for data-driven trajectory extraction from AIS data -- the $α$-method
por: Paulig, Niklas, et al.
Publicado: (2024)