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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
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Ethical review of real-world study
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
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
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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
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Estimating the effect of healthcare-associated infections on excess length of hospital stay using inverse probability-weighted survival curves [PDF]
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
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Understanding confounding effects in linguistic coordination: an information-theoretic approach [PDF]
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
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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
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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]
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]
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
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