In statistical models, confounding is said to occur when the apparent dependence of the response on a predictor is partially or wholly due to the dependence of both on a third variable not included in the model.

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Infer causality with high collinearity

I recently started to ask myself how to measure the impact of education on indexes like GDP: what is the outcome of mathematics or computer science on GDP, at the country level for instance. In this ...
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Random forest: confounding factors

I have N variables in K samples. There is a classification variable, T (treatment), and a confounding variable -- sex. Unfortunately, in the "no treatment" (CTRL) group there are significantly more ...
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53 views

What is the purpose of precision variables?

Why do we need to include precision variables in a regression model (i.e., a variable that is associated with the outcome but not the predictor of interest)?
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Including confounders in a model

Suppose that you have performing a linear regression examining the main effect $x_1$ and want to adjust for possible confounders $x_2, x_3, x_4$. Is it better to have an unadjusted model and a model ...
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Multicollinearity when adding a confounding variable

When you run a regression on ice cream sales with predictor shark attacks, you find a significant coefficient. But that is because there is a confounding variable temperature. But how do you correct ...
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101 views

Whitening data before regression, should I whiten the response variable too?

I have some data X where the samples are not independent (they're correlated with each other), and I'm trying to do a regression of some continuous variable y on X. This sample correlation could ...
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57 views

Purposeful selection and confounding

I conducted purposeful selection as outlined in Jewell's Statistics for Epidemiology. The log likelihood tests showed covariates, which I considered to be confounding though not significant in the ...
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267 views

How to calculate permutations of categorical variables with R

I want to simulate or calculate probabilities of combinations of group membership for different sample sizes (e.g., n= 3, 4, 5, 10, or 100) for two groups (of the same sample size). Each outcome could ...
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25 views

Discounting effect of one variable on another variable

Hope I manage to explain this simply enough so I don't end up confusing myself and you along with me! I've measured a variable (rate of soil respiration) throughout the day. However my variable is ...
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What examples of lurking variables in controlled experiments are there in publications?

In this paper: Lurking Variables: Some Examples Brian L. Joiner The American Statistician Vol. 35, No. 4, Nov., 1981 227-233 Brian Joiner claims that "randomization is not a panacea". This is ...
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23 views

Group comparison: match on variable

I am comparing two groups (A and B). Group B is matched to group A on variable X, by sampling two subjects from a population for each subject in group A. Group A has ~200 subjects, group B has ~400 ...
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34 views

Dealing with perfectly confounded microarray experiment

I need to compare microarray data, where all of the "cases" were hybridized in one batch and all of the "controls" in another, so I have no way of removing this batch effect. What would be the best ...
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37 views

Case-control study with data collected in batches

I have (matched) case-control data. The data is collected in batches in such a way that the batch determines some quality of the data (there is a 'batch' effect). Also, the cases and controls were ...
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Factor Elimination in Microarray data analysis

Many researchers must have issues with unwanted variance between experiments, be it technical or otherwise. What solutions are the most appropriate and commonly used to deal with (eliminate) such ...
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102 views

Accounting for sampling bias in Random Forest Model

I know I have significant annual variation due to observer bias in my data and also unequal sample sizes between years. I wanted to account for this by using "year" as a nuisance variable in a random ...
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46 views

Confounder - definition

According to M. Katz in his book Multivariable analysis (Section 1.2, page 6), "A confounder is associated with the risk factor and causally related to the outcome." Why must the confounder be ...
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2answers
106 views

How can I convert my dataset into a new dataset which is adjusted for confounding covariates?

I have used software before to do linear regression and factor in/out the confounding variables, but what I would like to do is generate a new data set which is adjusted for the confounding variables. ...
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45 views

(Conceptual framework) Should confounding factors be placed in the framework?

Suppose we study the effect of a factor X on an outcome Y. We hypothesize that there may be a potential confounder Z, which may or may not be observable or present in the data. Controlling for Z is ...
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192 views

Hypothesis test on data with confounding spatial clustering

This is a bit of an elaboration on a question I posted earlier, since I feel like my approach to the problem as a whole is probably quite flawed. Suppose I have a set of treatment and control cells, ...
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154 views

Can ANCOVA disagree with multiple regression?

I have 3 categorical variables (CVa, CVb, CVc) all 0 or 1. Two continuous variables (IV1, IV2) are confounding my observational study. The multiple regression ...
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322 views

Does adjustement completely remove the effect of the confounding variables?

This might seem a silly question but I am really confused about it. In theory adjusting for a confounder variable should remove its effect. Is this always true? and does this mean that the effect of ...
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102 views

Visualizing association results after adjusting for confounders

I'm trying to find a way to visualize the results of an association analysis where I corrected for confounding variables. I have a set of cytokine data (amount of protein in the blood) from a set of ...
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193 views

Adjusting for confounding variables in binary response variables

I have a dataset of patient information and I'm looking to find a way to compare two groups of patients and take into account confounding variables. My dataset has an N of ~1500 and I'm looking for a ...
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140 views

Techniques for analyzing ratios

I am looking for advice and comments that deal with the analysis of ratios and rates. In the field in which I work analysis of ratios in particular is widespread but I have read a few papers that ...
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114 views

Can lack of main effect and lack of interaction be caused by the same confound?

Can the lack of main effect have the same underlying cause as the lack of interaction in 2-way ANOVA? My results failed to reach significance for variables of gender and language. Is it possible that ...
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94 views

Can a confounding variable be correlated with the DV and not the IV?

Can a confounding variable be correlated with the DV and not the IV? I have heard of the DV being corr. but I can't find IV in any textbooks. I found this def. in Wiki: ...
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Examples of a confounding variable [duplicate]

Possible Duplicate: Correlation does not mean causation What is your favorite example of a confounding variable / confounding effect?
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Is it possible to have a variable that acts as both an effect modifier and a confounder?

Is it possible to have a variable that acts as both an effect (measurement) modifier and a confounder for a given pair of risk-outcome associations? I'm still a little unsure of the distinction. I've ...
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Using z-standardization to account for covariate

I would like to know whether z-standardization is an appropriate way to account for a covariate. Please consider the following dummy example (I am not interested in the interpretation of the result ...
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Understanding regression results when data are subsetted

I have some data that span several years: 2006-2010. I have run logistic regression to model the data. For the whole dataset, I get a 95% confidence interval for the odds ratio of a parameter of ...
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1answer
254 views

Which test to use to check if a possible confounder impacts a 0 / 1 result?

I've been given a task with the following question: Investigate whether or not the type or treatment (0 or 1) has an impact on the result (0 or 1) The same as 1), but keeping in mind another ...
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103 views

Checking whether or not a variable has impact

For a statistics assignment, I've been given a data set (regarding drug prevention) and a few questions. One of the questions is to check whether or not the choice of treatment, treatment A or ...
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72 views

Account for group age differences

I have a dataset of two patient and one healthy control group which I would like to compare (using R) with respect to a continuous outcome variable (each subjects is measured once). However the groups ...
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187 views

What to conclude when you fail to find an association in an epidemiological study?

Normally when somebody finds an association in an epidemiological study people are quick to point out that it doesn't prove causality, that there are problems of missing co-founders, that it is at ...
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158 views

References about univariable vs multivariable variable selection

Suppose I have variables $X_j$, $j=1,\ldots,p$, some of which are correlated, and some continuous output $y$. I want to rank the variables by importance. One way is to do an association test of each ...
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180 views

Please help to understand Agresti's book

I need some help to understand some topics in Agresti's Categorical Data Analysis. In section 6.3.1 (p 231), he provided a model like: $$ \text{logit}(\pi_{ik})=\alpha+\beta x_i+\beta_k^Z $$ where ...
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Does control get us closer to or farther from causation?

In logistic regression with an N of 40,000, purchase decision is unrelated to price. However, with certain demographic variables controlled, price can show a positive coefficient of meaningful ...
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Basic Simpson's paradox

I have a question about something that my statistics teacher said about the following problem: There are two hospitals named Mercy and Hope in your town. You must choose one of these in which to ...
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Identification of confounder in a logistic regression model example in “Applied Logistic Regression”

I am reading Hosmer's Applied Logistic Regression, and I am a bit stuck in chapter 3, when taking about interaction and confounders. In page 77, it states the following: Using the estimated ...
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Overmatching bias and confounding variables

As I understand it, matching is one way to identify causality in observational studies. By matching observations that are "similar" and comparing ones that did or did not receive treatment, you can ...
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338 views

Simpson's paradox or confounding?

Consider a scenario where a two-way contingency table is analyzed by a chi-squared test of independence and a significant result is found. Now, it turns out that this table is an aggregation of data ...
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Testing for confounding

Suppose we want to test whether $Z$ is a confounding variable for the effect of $X$ on $Y$. Is it enough to just check unadjusted and adjusted estimates of the coefficient of $X$ and see if they ...
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Non-parametric test with several confounding factors

I am pretty new to statistical testing with confounding variables, so please excuse if there will be some gross misunderstandings or basic questions. I want to test for systematic differences in ...
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How exactly does one “control for other variables”?

Here is the article that motivated this question: Does impatience make us fat? I liked this article, and it nicely demonstrates the concept of “controlling for other variables” (IQ, career, income, ...
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Differences in coefficients

Suppose I want to see whether $z$ is a confounder for a model with $y$ the outcome variable and $x$ the predictor. If I adjust for $z$, and the adjusted coefficient of $x$ changes versus the ...
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649 views

How to identify suitable variables to assess confounding, mediation and effect modification?

Imagine that you are planning a study about risk behaviours among HIV positive injecting drug users. All the individuals included in the sample are injecting drugs and all are HIV positive. The main ...
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312 views

How does the size of a dataset affect confounding in both randomised trials and observational studies?

I have heard that larger sample sizes in randomised trials lead to a smaller possibility of confounding. Why is this true in the case of randomised trials? Also, how does sample size of an ...
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Feature selection for classification, controlling for sub-population

I have a bunch of points that belong to one of population P1, P2, ... Pn AND to class A or B. Within each population I'll be doing classification between A and B, and I want to select features that ...