Cargando…

Local Instrumental Variable Methods to Address Confounding and Heterogeneity when Using Electronic Health Records: An Application to Emergency Surgery

BACKGROUND: Electronic health records (EHRs) offer opportunities for comparative effectiveness research to inform decision making. However, to provide useful evidence, these studies must address confounding and treatment effect heterogeneity according to unmeasured prognostic factors. Local instrume...

Descripción completa

Detalles Bibliográficos
Autores principales:	Moler-Zapata, Silvia, Grieve, Richard, Lugo-Palacios, David, Hutchings, A., Silverwood, R., Keele, Luke, Kircheis, Tommaso, Cromwell, David, Smart, Neil, Hinchliffe, Robert, O’Neill, Stephen
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	SAGE Publications 2022
Materias:	Original Research Articles
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9583279/ https://www.ncbi.nlm.nih.gov/pubmed/35607984 http://dx.doi.org/10.1177/0272989X221100799

Ejemplares similares

Effectiveness of emergency surgery for five common acute conditions: an instrumental variable analysis of a national routine database
por: Hutchings, A., et al.
Publicado: (2022)

Variation in the rates of emergency surgery amongst emergency admissions to hospital for common acute conditions
por: Hutchings, Andrew, et al.
Publicado: (2021)

Impact of the first wave of COVID-19 on outcomes following emergency admissions for common acute surgical conditions: analysis of a national database in England
por: Hutchings, Andrew, et al.
Publicado: (2022)

Major Potential Confounder Not Addressed
por: Vines, Jennifer
Publicado: (2006)

Analysis of Benefit of Intensive Care Unit Transfer for Deteriorating Ward Patients: A Patient-Centered Approach to Clinical Evaluation
por: Grieve, Richard, et al.
Publicado: (2019)

Ethnic and socioeconomic disparities in initiation of second‐line antidiabetic treatment for people with type 2 diabetes in England: A cross‐sectional study
por: Bidulka, Patrick, et al.
Publicado: (2022)

Instrumental variables as bias amplifiers with general outcome and confounding
por: Ding, P., et al.
Publicado: (2017)

Addressing confounding artifacts in reconstruction of gene co-expression networks
por: Parsana, Princy, et al.
Publicado: (2019)

Uncovering interpretable potential confounders in electronic medical records
por: Zeng, Jiaming, et al.
Publicado: (2022)

Addressing the Path-Length-Dependency Confound in White Matter Tract Segmentation
por: Liptrot, Matthew G., et al.
Publicado: (2014)

A new method to address unmeasured confounding of mortality in observational studies
por: Tannen, Richard, et al.
Publicado: (2016)

Inconsistent seduction: Addressing confounds and methodological issues in the study of the seductive detail effect
por: Tislar, Kay L., et al.
Publicado: (2021)

Identification of the outcome distribution and sensitivity analysis under weak confounder–instrument interaction
por: Mao, Lu
Publicado: (2022)

MASE: An Instrument Designed to Record Underwater Soundscape
por: Rodríguez-Méndez, Iván, et al.
Publicado: (2022)

Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies
por: Yoon, Jin H., et al.
Publicado: (2021)

Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables
por: Zawadzki, Roy S., et al.
Publicado: (2023)

Confounding
por: Andrade, Chittaranjan
Publicado: (2007)

Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders
por: Yi, Grace Y., et al.
Publicado: (2023)

Confounding by ill health in the observed association between BMI and mortality: evidence from the HUNT Study using offspring BMI as an instrument
por: Carslake, David, et al.
Publicado: (2018)

Delimitation despite discordance: Evaluating the species limits of a confounding species complex in the face of mitonuclear discordance
por: Firneno, Thomas J., et al.
Publicado: (2021)

Accounting for confounding
por: Raina, Sunil Kumar
Publicado: (2015)

Measuring away an attentional confound?
por: Morales, Jorge, et al.
Publicado: (2017)

Confound-leakage: confound removal in machine learning leads to leakage
por: Hamdan, Sami, et al.
Publicado: (2023)

Adjustment for unmeasured confounding through informative priors for the confounder-outcome relation
por: Groenwold, Rolf H. H., et al.
Publicado: (2018)

A novel method for controlling unobserved confounding using double confounders
por: Liu, Lu, et al.
Publicado: (2020)

Instrumental variable-based high-dimensional mediation analysis with unmeasured confounders for survival data in the observational epigenetic study
por: Chen, Fangyao, et al.
Publicado: (2023)

Developmental Trajectories in Diagnostic Reasoning: Understanding Data Are Confounded Develops Independently of Choosing Informative Interventions to Resolve Confounded Data
por: Moeller, April, et al.
Publicado: (2022)

Antibiotics as first-line alternative to appendicectomy in adult appendicitis: 90-day follow-up from a prospective, multicentre cohort study
por: Javanmard-Emamghissi, H, et al.
Publicado: (2021)

Reply to Suissa: Mortality in IMPACT: Confounded by Asthma?
por: Lipson, David A., et al.
Publicado: (2020)

Dietary Research on Coffee: Improving Adjustment for Confounding
por: Thomas, David R, et al.
Publicado: (2019)

Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records
por: Low, Yen Sia, et al.
Publicado: (2016)

Confusion worse confounded
por: Noronha, Vanita, et al.
Publicado: (2013)

Confounding by linkage disequilibrium
por: Aissani, Brahim
Publicado: (2014)

Confusion Worse Confounded
Publicado: (1891)

Confusion Worse Confounded
Publicado: (1890)

Principles of confounder selection
por: VanderWeele, Tyler J.
Publicado: (2019)

Confounds in “Failed” Replications
por: Bressan, Paola
Publicado: (2019)

Confusion Worse Confounded
Publicado: (1907)

Misspecification of confounder-exposure and confounder-outcome associations leads to bias in effect estimates
por: Schuster, Noah A., et al.
Publicado: (2023)

Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding
por: Latimer, NR, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_cva5ahl9b5fro19ck4266751ok