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Confounding [PDF]

open access: yesNephron Clinical Practice, 2010
In confounding, the effect of the exposure of interest is mixed with the effect of another variable. It is important to identify relevant confounders and remove the confounding effect as much as possible. There are three criteria that need to be fulfilled to determine whether a variable could be considered a potential confounder. The first criterion is
Stralen, K.J. van   +3 more
  +8 more sources

Ethical review of real-world study

open access: yesРеальная клиническая практика: данные и доказательства, 2022
This article is devoted to an ethical review of planned real-world studies. The legal basis of such examinations has also been considered. Most real-world studies are non-interventional, so the ethical review of such studies is similar to that of ...
E. A. Volskaya   +2 more
doaj   +1 more source

Sensitivity analysis for causal effects with generalized linear models

open access: yesJournal of Causal Inference, 2022
Residual confounding is a common source of bias in observational studies. In this article, we build upon a series of sensitivity analyses methods for residual confounding developed by Brumback et al. and Chiba whose sensitivity parameters are constructed
Sjölander Arvid   +2 more
doaj   +1 more source

Can we diagnose mental disorders in children? A large‐scale assessment of machine learning on structural neuroimaging of 6916 children in the adolescent brain cognitive development study

open access: yesJCPP Advances, 2023
Background Prediction of mental disorders based on neuroimaging is an emerging area of research with promising first results in adults. However, research on the unique demographic of children is underrepresented and it is doubtful whether findings ...
Richard Gaus   +4 more
doaj   +1 more source

Estimating the effect of healthcare-associated infections on excess length of hospital stay using inverse probability-weighted survival curves [PDF]

open access: yes, 2020
Background: Studies estimating excess length of stay (LOS) attributable to nosocomial infections have failed to address time-varying confounding, likely leading to overestimation of their impact.
Batra, Rahul   +5 more
core   +2 more sources

Understanding confounding effects in linguistic coordination: an information-theoretic approach [PDF]

open access: yes, 2015
We suggest an information-theoretic approach for measuring stylistic coordination in dialogues. The proposed measure has a simple predictive interpretation and can account for various confounding factors through proper conditioning.
Galstyan, Aram   +2 more
core   +6 more sources

A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures

open access: yesJournal of Causal Inference, 2022
Analysts often use data-driven approaches to supplement their knowledge when selecting covariates for effect estimation. Multiple variable selection procedures for causal effect estimation have been devised in recent years, but additional developments ...
Talbot Denis, Beaudoin Claudia
doaj   +1 more source

Standardization of Therapeutic Measures in Antibiotic Consumption Monitoring to Compare Different Livestock Populations

open access: yesFrontiers in Veterinary Science, 2020
Using sales data, information on antimicrobial consumption in animals is collected cumulatively across the European Union and member countries of the European Economic Area, which is documented and reported by every country and published within annual ...
Katharina Hommerich   +4 more
doaj   +1 more source

Confounding and confounders [PDF]

open access: yesOccupational and Environmental Medicine, 2003
Confounding should always be addressed in studies concerned with causality. When present, it results in a biased estimate of the effect of exposure on disease. The bias can be negative - resulting in underestimation of the exposure effect - or positive, and can even reverse the apparent direction of effect.
openaire   +3 more sources

Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders [PDF]

open access: yes, 2021
Random effect models are popular statistical models for detecting and correcting spurious sample correlations due to hidden confounders in genome-wide gene expression data.
Malik, Muhammad Ammar, Michoel, Tom
core   +2 more sources

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