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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18 views

Tricky confounding between definition of exposure and primary outcome: how do I resolve this in a regression analysis?

Interesting confounding problem involving in what should otherwise be a pretty straightforward regression analysis: I am interested in whether being on a drug (E=exposure) during a course of therapy ...
3
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1answer
101 views

Adjusting for confounders when comparing means with t test

In an article I found recently they were able to compute the difference in means of two groups (presumably with a t-test) while adjusting for confounders; they called this the aDiff (adjusted ...
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2answers
40 views

Identifying a confounder

I'm trying to check whether a variable is a confounder or not. Specifically, for a randomized trial where I want to investigate the effects of a reduction in class size on student performance, would ...
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0answers
12 views

Adding confounding variables into R's lm function [duplicate]

I'm looking at the 'mtcars' dataset and trying to understand how differences in weight (wt) and/or cylinder (cyl) counts affect mileage per gallon (mpg). How does one "adjust" for confounding ...
3
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21 views

Adjusting confounders

I'm conducting a case-control study consisted of 32 males and 35 females matched by age and gender with controls. Do I have to control for gender when I do the statistical analysis?
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20 views

Adjusting for confounders when the investigated exposures are gene mutations

I'm delving into causality and directed acyclic graph for choosing the right covariate structure for multivariable regression analysis. Reading Pearl work, I understood that one should adjust only ...
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1answer
29 views

Correlation or Confounding? (Linear Regression)

I have some data in which some yield percentages are given for different temperatures and stirring rates. I calculate the correlation coefficient (r) in each case. Say $r_1$ is for temperature and ...
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2answers
55 views

Adjusting for Confounding with Kruskal Wallis?

I have a numerical response variable A which depends on a categorical explanatory variable B. I also have another variable C that I'd like to check for confounding effects. So far I've been using ...
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1answer
50 views

Unconfoundedness in Rubin's Causal Model- Layman's explanation

When implementing Rubin's causal model, one of the (untestable) assumptions that we need is unconfoundedness, which means $$(Y(0),Y(1))\perp T|X$$ Where the LHS are the counterfactuals, the T is the ...
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1answer
26 views

Including already balanced confounders in propensity score model

I have a dataset that I want to run propensity score analysis on. Using package TWANG in R, I plan to compute the propensity score and use it as IPTW. The variables that I put into the model are those ...
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1answer
20 views

Adjust age as confounding factor

I have a continuous response variable (concentration) and a categorical explanatory variable (healthy/ill), and probably two confounding factors: age (continuous) and gender. What would you ...
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13 views

R LIMMA Longitudinal analysis adjusting for continuous variables

I'm analysing a longitudinal gene expression qPCR array study in LIMMA. I have 3 groups with baseline and week 48 measurements. I also have 2 confounding baseline continuous variables that I would ...
2
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1answer
42 views

How to highlight a confounding variable?

Suppose we study the effect of random variable $X$ on $Y$ and we suspect a third variable $C$ to be a confounder. Is there a sound way to highlight this suspicion ? I can imagine showing the ...
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31 views

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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2answers
161 views

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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0answers
16 views

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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1answer
24 views

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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2answers
54 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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1answer
33 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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25 views

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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83 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
75 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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1answer
49 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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27 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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1answer
82 views

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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54 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
27 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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15 views

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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24 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
147 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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56 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
139 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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269 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
46 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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36 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
307 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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171 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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0answers
46 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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30 views

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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1answer
214 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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17 views

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
77 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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43 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
43 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 ...
3
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1answer
233 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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1answer
472 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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3answers
527 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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2answers
635 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 ...
2
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2answers
405 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 ...