The green variable represents the exposure variable; the blue variable represents the outcome variable; grey variables represent unobserved variables; pink variables represent confounders; pink lines show biasing paths; green lines show causal paths. Additional demographics included proportion of high income earners, proportion of elderly residents, or proportion of non-Hispanic White residents depending on the model. Bivariate associations between each covariate and the CRI were assessed with Pearson correlations when continuous and Gaussian (Proportion of population ≥ 65 yrs of age, Property Crime per 100,000 in 2016, ICEincome, ICEeducation) and with a Spearman’s rank correlation when the variables were not Gaussian. Some of the covariat...
© 2018, Sociedad Medica de Santiago. All rights reserved. Background: Confusion in observational epi...
<p>Directed Acyclic Graph (DAG) showing the network of relationships between the population growth r...
The issue of confounding, and the bias it can induce, is a key concern in epidemiology, and yet ther...
<p>Instrumental variable models use associations C and A to estimate the causal effect of a risk fac...
Please note that the hypothesised DAG represents simplified associations for confounders and mediato...
<p>Directed acyclic graph (DAG) used to guide the analysis and showing hypothesized relationships be...
The goal of most epidemiological studies is to determine an unbiased estimate of the effect of being...
BACKGROUND: Correlated data are ubiquitous in epidemiologic research, particularly in nutritional an...
Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are...
Race, age, gender, education, income/wealth, and neighborhood exposures all have direct effects on D...
<p>All lines represent significant conditional associations. The numbers indicate the partial γ-coef...
Epidemiologists typically seek to answer causal questions using statistical data:we observe a statis...
International audienceABSTRACT: BACKGROUND: Directed acyclic graphs (DAGs) are an effective means of...
<p>Direct Acyclic Graph (DAG) for the association of low educational status and Major Cardiovascular...
Background: Directed acyclic graphs, or DAGs, are a useful graphical tool in epidemiologic research ...
© 2018, Sociedad Medica de Santiago. All rights reserved. Background: Confusion in observational epi...
<p>Directed Acyclic Graph (DAG) showing the network of relationships between the population growth r...
The issue of confounding, and the bias it can induce, is a key concern in epidemiology, and yet ther...
<p>Instrumental variable models use associations C and A to estimate the causal effect of a risk fac...
Please note that the hypothesised DAG represents simplified associations for confounders and mediato...
<p>Directed acyclic graph (DAG) used to guide the analysis and showing hypothesized relationships be...
The goal of most epidemiological studies is to determine an unbiased estimate of the effect of being...
BACKGROUND: Correlated data are ubiquitous in epidemiologic research, particularly in nutritional an...
Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are...
Race, age, gender, education, income/wealth, and neighborhood exposures all have direct effects on D...
<p>All lines represent significant conditional associations. The numbers indicate the partial γ-coef...
Epidemiologists typically seek to answer causal questions using statistical data:we observe a statis...
International audienceABSTRACT: BACKGROUND: Directed acyclic graphs (DAGs) are an effective means of...
<p>Direct Acyclic Graph (DAG) for the association of low educational status and Major Cardiovascular...
Background: Directed acyclic graphs, or DAGs, are a useful graphical tool in epidemiologic research ...
© 2018, Sociedad Medica de Santiago. All rights reserved. Background: Confusion in observational epi...
<p>Directed Acyclic Graph (DAG) showing the network of relationships between the population growth r...
The issue of confounding, and the bias it can induce, is a key concern in epidemiology, and yet ther...