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
0answers
12 views

Describing the causal effect of coffe and heat on esophageal tumor. Interaction, confounding, mediation?

The public health world is discussing in these days the news that coffee is no longer considered carcinogenic but heat of the drinks is the real culprit. I'm trying to visualizing this as a causal ...
2
votes
0answers
4 views

Can I use Covariate Balancing Propensity Score method to adjust for confounding in a Cox Regression model with splines?

I would like to use the Covariate Balancing Propensity Score (CBPS) method to adjust for confounding because of its optimization properties. I am doing a Cox regression model on some observed ...
0
votes
1answer
16 views

Covariates and degrees of freedom

Consider a problem, when one is interested in finding influence of variable set {X} on Y (assuming reverse causal relation is infeasible), when confounding influences from a set of variables {Z} are ...
1
vote
0answers
21 views

Selection Bias and Controlling Covariates

I am currently performing a retrospective study that is comparing a surgical procedure vs a modified version of the same procedure. There is obvious selection bias because of the selection criteria ...
0
votes
1answer
23 views

What is the difference/relation between variables that are multicollinear, confounding, interacting

What is the exact difference between two variables that are multicollinear and two variables that are interacting and two variables that are confounding? Are they multiple meanings for the same thing?...
0
votes
0answers
7 views

General strategy of confounding design

Example: Suggest a confouding scheme for a $2^8$ experiment in 16 blocks of 16, assuming that all 2-factor and 3-factor interactions are to be estimated. Please find the confounding design. Question: ...
3
votes
0answers
27 views

Confounding variable vs lurking variable

I realize that there there are countless examples of this on the web however I am still have some difficulty with it. I was just hoping to get some feedback on my interpretation. In a lurking ...
0
votes
0answers
8 views

Controlling for a variable highly correlated with the variable of interest

I want to see if there's a relationship between $x$ and $y$. A variable $z$ is highly (but not perfectly) correlated with $x$. I want to check that $z$ is only related $y$ through $x$, and not ...
1
vote
0answers
15 views

Confounding variable that is the focus of experiment

The wiki defines confounding variable as an extraneous variable in a statistical model that correlates (directly or inversely) with both the dependent variable and the independent variable I'...
2
votes
1answer
46 views

Multivariable survival analysis: adding another variable lowers the p value?

When I was performing the Cox survival analysis on my data, I tried to look at the predictive value of different variables to survival. For example, here I have two variables: 'size' and 'surface'. ...
4
votes
4answers
140 views

Why does propensity score matching work for causal inference?

Propensity score matching is used for make causal inferences in observational studies (see the Rosenbaum / Rubin paper). What's the simple intuition behind why it works? In other words, why if we ...
1
vote
0answers
32 views

Difference between confounding and aliasing in 2^k factorial design

In statistics, particularly in experimental design, what is the difference between confounding and aliasing in 2^k factorial designs? Also how is a principal block related to the two concepts? I've ...
10
votes
3answers
669 views

Do we really need to include “all relevant predictors?”

A basic assumption of using regression models for inference is that "all relevant predictors" have been included in the prediction equation. The rationale is that failure to include an important real-...
0
votes
0answers
51 views

Determine if covariate is confounding in Cox proportional hazards model

I've developed a risk score that predicts patient survival. Now I want to see whether my risk score is independent of cancer stage. I've already determined that there's no interaction between the two, ...
1
vote
2answers
82 views

How to “statistically adjust” for variables? [duplicate]

I've been trying to understand what means "statistically adjusted" when comparing two variables. For example, when computing the odds ratio for a death after surgery in two hospitals, we compute the ...
0
votes
0answers
57 views

dependent and independent risk factor

when a certain association appears only when we adjusted for certain potential confounders. In this case, can we say that this association is independent of this confounder. in my case, I'm studying ...
0
votes
0answers
28 views

How to choose between data-driven pattern or intuition?

I am performing a multivariate logistic regression (This could very well be any other kind of regression method) to study the effect of some predictor variables on the probability of event. I have ...
0
votes
0answers
22 views

Partial Correlation - Quantifying the effect of removing confounding variables on the correlation

I'm conducting partial correlation in order to quantify the association between two variables, X and Y, after the effect of a set of confounding variables Z has been removed from both X and Y. In ...
2
votes
1answer
31 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
votes
1answer
220 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 ...
1
vote
2answers
49 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 ...
0
votes
0answers
13 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
votes
1answer
23 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?
0
votes
0answers
40 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 ...
1
vote
1answer
39 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 ...
1
vote
2answers
111 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 ...
1
vote
1answer
69 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 ...
2
votes
1answer
35 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 ...
0
votes
1answer
28 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 ...
0
votes
0answers
34 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
votes
1answer
49 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 ...
0
votes
0answers
46 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). ...
5
votes
2answers
231 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 ...
0
votes
0answers
18 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 $r_{X}^{2}\...
1
vote
1answer
26 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 ...
4
votes
2answers
69 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 ...
4
votes
1answer
36 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 ...
0
votes
0answers
28 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 ...
1
vote
2answers
95 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, ...
0
votes
1answer
81 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. ...
0
votes
1answer
67 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 ...
3
votes
1answer
85 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
1answer
28 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
16 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
25 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
197 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
57 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
208 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 ...
8
votes
2answers
293 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
51 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. ...