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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How to correct the means of a variable in 4 groups matlab

I compute the mean of the variable Y in 4 groups (A B C D) that differ for age, gender and body mass index (BMI). ...
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regression analysis with confounding variables, how to interpret your main coefficient when controlling for confounders

I'm interested in the effect of X on Y and want to adjust for confounding variables in my regression model. If the model (regression, F-test) is not significant but the predictor of which I'm ...
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Canceling a confound with linear regression?

I would like to compare variables $X$ and $Y$. However, I notice that both are effected by some confounding variable $Z$. I do linear regression on $X$ and $Y$ with $Z$ and find that ...
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How to address unintended confounding in an experiment

I am conducting a study looking at the effects of 3 different footwear conditions on energy expenditure during running. This will be a repeated measures study. I want to control for the effects of ...
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36 views

Correct and clear wording for non-causal correlation

Despite reading multiple statistics and epidemiology texts as well as studies, I have trouble describing the following in plain English for a public of doctors (so, non-statisticians or biomedical ...
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29 views

Why does randomising the order of measurements remove time as a confounding variable?

Say we're interested in the difference in x between Group1 and Group2. We might measure 50 samples from Group1 then 50 samples from Group2. If the accuracy or precision of our measurements change over ...
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Is gravity a viable confounding variable in this scenario?

The two variables are: The width of an elevator door The brake force of an elevator The width of an elevator door is associated with its emergency brake force. What are some possible confounding ...
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71 views

Insignificance by confounding variables

I am confused about a result in my OLS regression. I am regressing health on both crime level and ubanization and a couple of commonly encountered covariates in the literature such as, for example, ...
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1answer
58 views

Methodological question: adjusting for confounders in logistic regression

I have three attributes in a dataset (D0), representing the binary response of success or failure (R), some form of treatment or treatment group (T), and a potential confounder (C) respectively. ...
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32 views

Should I keep or eliminate an insignificant confounding variable?

Let's say that I am fitting a logistic regression model for a binary outcome and I have two covariates: $x_1$ and $x_2$ (both quantitative). I am confused as to what the correct course of action ...
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24 views

Have I interpreted the effect of a confounding variable correctly?

I'm interested in whether feeding rate differ between two species of birds, Species A and Species B. However, tide also affects their feeding rate, and so tide is a confounding variable. In the plot ...
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May I use the whole dataset to prove the existence of a confounding variable in a machine learning framework if I don't use the labels?

I have a certain dataset that I am analyzing with machine learning techniques. I believe there is a certain variable (not used for training or testing the classifiers but is still known) that has an ...
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35 views

Confounding factor in cross-validation

I have been exploring a dataset using support vector machines. I am solving a binary classification problem and using stratified K-fold cross-validation for performance estimation (the SVM ...
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1answer
26 views

Can a study be “confounded” by chance?

This is a question about the definition of confounding, and/or about statistics pedagogy. Suppose that you're doing a study to see if $X$ and $Y$ are associated, but they are not. Unbeknownst to you, ...
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Isolating influence of sampling from actual change

Say I want to evaluate teams' batting coaches in a hypothetical baseball league. It's an unusual league in that there is no control over (and large fluctuation within) the number of at-bats each ...
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20 views

Controls sampled on confounding variable

Let's say I wanted to use logistic regression to analyze the effect of an exposure variable on a categorical outcome variable ("yes" or "no"). I believe there are two important confounding variables ...
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1answer
96 views

Factor analysis to remove noise

Performing factor analysis/PCA to remove potential hidden latent variables from high dimensional data is extremely useful to remove confounding/noise/measurement error and batch effects. However, ...
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50 views

Can 'selection bias' refer to bias in the intervention as well as in the sampling?

I have been using the term selection bias to refer to a situation where (e.g.) schools with certain pre-existing characteristics are more likely to be included in (e.g.) a teacher training programme ...
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1answer
90 views

Stratified concordance index (survival::survConcordance)

What is the idea of having a stratified concordance (C-index) in survival::survConcordance, as opposed to computing the concordance over all samples ignoring the strata? Can there be some inflation ...
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242 views

A potential confound in an experiment design

Overview of the question Warning: This question requires a lot of set-up. Please bear with me. A colleague of mine and I are working on an experiment design. The design must work around a large ...
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1answer
38 views

Transformation necessary or look for confounding variables

I've read through the most popular threads concerning confounding variables, but I haven't been able to find an answer to my specific question. Sorry for the wall of text, I hope it's clear enough. ...
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Finding an Alias pattern (Confounding) in fractional factorial design

I have basic generating relation. I=ACE=FBD=GCD=ABCH=ABDJ=ACDK=BCDB1=ABCDB2 As far as I understand I also need to find all possible combination of the basic, then I have complete generating ...
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34 views

Include confounder into partial least squares regression

I am wondering, when using partial least squares regression to investigate a research question, there is predictor component (T) and response component (U), if I want to adjust for confounders (C), do ...
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1answer
261 views

Ignore strata in external validation of stratified Cox prop hazards model?

I've fit a stratified Cox proportional hazards model to some survival data, where I've stratified by a potential confounder which is the batch the data comes from (there are three batches). Now, I'd ...
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131 views

Confounding vs. effect modification in 2x2 tables

I'm doing a statistics course at the moment and have a quick question about effect modification and confounding, and working out which is which in 2x2 tables in a case-control stud. I have three ...
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38 views

Multilevel propensity score

I'm trying to analyze many treatments on outcome after propensity score 1:1 matching. My problem: I have 6 differents drugs and each patient can take or not each of these. If I build my propensity ...
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Help with real examples of non-confounders

I am looking for some real clinical examples for variables that are NOT confounders: 1) variables only impact treatment, but not outcome. 2) variables only impact outcome, but not treatment So the ...
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Is this a confounding interaction: using demographic data in a fractional factorial design?

I've created and run a choice experiment (conjoint analysis) using a fractional factorial design 3 x 3 x 3 x 3 (four factors with three levels each). I also collected some demographic data (age, ...
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148 views

Statistical issues with aggregating annual survey data from multiple years?

I am using a national telephone survey conducted every year by the CDC called the Behavioral Risk Factor Surveillance System (BRFSS) to answer a question about breast cancer screening rate in ...
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1answer
16 views

Find the effect of a attribute value on an outcome by eliminating confounding values

I have a series of lets say five attributes. The first attribute is called diagnosis code 1, the second diagnosis code 2 etc. The values are codes which represent diseases. In other words, each ...
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Dropped cases from matched studies

We have cohort data and a rare exposure which we are matching to controls in a large epidemiologic dataset. The matching variable is a deidentified neighborhood indicator (cluster) which guarantees ...
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1answer
64 views

Issue with controlling confound in multinomial regression analysis; different results when removing kids on meds

I examined the influence of ADHD on abnormal bodyweight in a very large, national sample of children. In my multinomial regressions, I controlled for several specific confounds, which have been shown ...
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35 views

Google trends data for interest

I was discussing about the popularity of some terms and used google trends to conclude in the decrease of their popularity. Here is an exemple of the queries for some of the biggest french ...
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1answer
39 views

Alias Structure of one-fourth replicate of a $4^2$-Factorial Design with interaction $\text{A}{B}^3$ confounded

For finding the Alias of main effect A , i started as the following : $\text{A}$$\times$ $\text{A}{B}^3$$=\text{A}^2\text{B}^3={(\text{A}^2\text{B}^3})^2=\text{A}^4\text{B}^6=\text{B}^2$(mod ...
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1answer
192 views

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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317 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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376 views

Including confounders in a model

Suppose that you are 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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453 views

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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341 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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1answer
175 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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1answer
430 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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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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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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1answer
57 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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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
161 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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1answer
268 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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1answer
224 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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1answer
689 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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2answers
127 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 ...