# Tagged Questions

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.

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 ...
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 ...
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 ...
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 ...
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?...
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: ...
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 ...
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 ...
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'...
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'. ...
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 ...
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 ...
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-...
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, ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
23 views

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?
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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). ...
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 ...
18 views

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 ...
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 ...
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, ...
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 ...
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 ...