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.

learn more… | top users | synonyms

0
votes
1answer
18 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 ...
0
votes
0answers
18 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 ...
4
votes
1answer
72 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 ...
0
votes
0answers
22 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 ...
0
votes
1answer
25 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, ...
1
vote
0answers
11 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 ...
0
votes
0answers
17 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 ...
2
votes
1answer
64 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, ...
6
votes
2answers
48 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 ...
1
vote
1answer
42 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 ...
5
votes
2answers
224 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 ...
1
vote
1answer
28 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. ...
0
votes
0answers
25 views

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 ...
1
vote
0answers
24 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 ...
0
votes
1answer
170 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 ...
0
votes
0answers
101 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 ...
0
votes
0answers
27 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 ...
1
vote
0answers
27 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 ...
0
votes
0answers
27 views

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, ...
1
vote
1answer
93 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 ...
0
votes
0answers
26 views

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 ...
0
votes
0answers
14 views

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 ...
0
votes
0answers
21 views

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 ...
1
vote
1answer
15 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 ...
0
votes
0answers
22 views

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, ...
0
votes
0answers
16 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 ...
2
votes
1answer
51 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 ...
1
vote
1answer
35 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 ...
0
votes
0answers
49 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 ...
3
votes
1answer
162 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 ...
2
votes
1answer
243 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)?
1
vote
3answers
238 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 ...
1
vote
2answers
345 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
votes
2answers
304 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 ...
2
votes
1answer
143 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 ...
5
votes
1answer
388 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 ...
8
votes
3answers
459 views

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 ...
1
vote
0answers
47 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 ...
1
vote
1answer
50 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 ...
3
votes
0answers
80 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 ...
2
votes
2answers
148 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. ...
4
votes
1answer
247 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, ...
2
votes
1answer
216 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 ...
5
votes
1answer
627 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 ...
0
votes
2answers
124 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 ...
0
votes
2answers
347 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 ...
10
votes
1answer
169 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 ...
1
vote
1answer
144 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 ...
0
votes
1answer
108 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: ...
0
votes
0answers
43 views

Examples of a confounding variable [duplicate]

Possible Duplicate: Correlation does not mean causation What is your favorite example of a confounding variable / confounding effect?