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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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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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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66 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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41 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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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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62 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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Can I examine the confounding effect of variables on non-normal data using Pearson?

I have used Kendall's tau to examine whether there is a correlation between a number categorical variables, as I have a small sample. However, I also want to test whether some variables might have an ...
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Find relations without confounding variables? [duplicate]

I have multiple numerical and categorical variables which I'd like to data-mine for simple relations. I'd create simple plots of two variables which are supposed to have a meaningful statement. Can ...
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counfounded batch effects in microarray dataset - can I do partial experiment redesign?

I'm working with a microarray data set where the batches are completely confounded treatment time, i.e. time t1 is all in batches b1 and b2, and time t2 is all in batches b3, b4, and b5. I know this ...
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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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Mixture model for dependent observations with additive group-level confounders

I'm looking for a special type of mixture model (described below) and I'm hoping to get some hints with regards to relevant literature to look at or names to be searching for. On the general level, ...
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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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39 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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29 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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38 views

Meta-analysis with known confounder

I’m performing a meta-analysis in which the main outcome of interest is a correlation coefficient between two variables, $X$ (a psychological measure) and $Y$ (a biomarker). $Y$ is known to be ...
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138 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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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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129 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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170 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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215 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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261 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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116 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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342 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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46 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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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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225 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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198 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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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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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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287 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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153 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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127 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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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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104 views

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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175 views

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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383 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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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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81 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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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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187 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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189 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 ...