Questions tagged [confounding]
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, or dependence on a linear combination of other variables included in the model. Confounding with a variable included in a model is often called multicollinearity. A synonym is *aliasing*, used in design of experiments.
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Analysis of GBS before and after COVID-19 while adjusting for confounding variables
I am new to statistics and would appreciate the help! I am using SPSS and am working on a project where I want to analyze the impact of COVID-19 on Group B Streptococcus (GBS). I therefore have 4 ...
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Regression lines adjusted for region vs region-specific regression lines
I would like to understand the difference between the regression lines in black and the regression lines in blue, (slide page 10) below.
In blue : regression lines adjusted for region, plotted ...
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Multicollinearity and estimation of correlation between time series in fMRI data
I have a fMRI data consisting of a set of time series for activity of each region of brain. There is a concept called functional connectivity which shows how activity of each region depend on other ...
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How can REs adjust for confounding if they are required to be uncorrelated with the FEs in the model?
How can random effects adjust for confounding if they are required to be uncorrelated with the fixed effects (explanatory variables) in the model?
This question explains that including a variable in a ...
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Definition of selection bias vs confounding bias
I've been learning about causal inference, having read Pearl's Primer and Parts I and II of "What If?".
I was under the impression that the definition of "There is confounding" was
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Simulating time-fixed confounder in time-varying survival model
I am attempting to simulate a survival Cox PH setting with time-varying exposure in R. The goal is assess the effect of a time-fixed confounder in the relationship exposure-outcome.
What I am trying ...
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Is this confounding bias or selection bias or both?
Can confounding and selection bias (biased sampling) be the same?
In epidemiology, selection bias and confounding are often considered as two different biases. I wonder if they can be same in certain ...
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Stepwise regression using Akaike's Information Criterion to control for potential confounders?
I am trying to determine the association between crime rates and economic inequality. My crime rate data is by U.S. county for several crime types (burglary, pickpocketing, assault, etc.). My economic ...
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Simpson's paradox in Freedman, Pisani and Purves book
In this book, there is an example of sex bias in graduate admissions.
Major
Men
Women
# applicants
% admitted
# applicants
% admitted
A
825
62
108
82
B
560
63
25
68
C
325
37
593
34
D
417
33
375
...
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Understanding Ignorability and Confounding Variables
I am reading Data Analysis Using Regression and Hierarchical Models and am confused by the concept of ignorability. The description in the book seems to say different things.
Said another way,
we ...
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Confounding Variable in Regression Model: Simpson's Paradox
I am working on a mixed effects regression model where Yi = exam score of student i.
The explanatory variables are the following:
Level 3: school type (public vs. private) and school's socioeconomic ...
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Can I interpret the size of my regression coefficient when I only control for confounders and not non-confounding covariates?
I am very confused at the moment, for my bachelor's thesis I am performing a Panel Individual Fixed Effects analysis and have only controlled for confounders.
I was under the assumption that when I ...
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How to deal with possibly important predictors omitted during the building of an OLS multivariate linear regression model?
I am building a descriptive model using OLS multivariate linear regression. I have a couple dozen candidate predictors, but only around 200 cases.
Since I wanted at least 10 cases / variable for the ...
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Shrinkage of covariates in the Cox model
In a regression model (e.g Cox model) when there are too few events to support modeling all desired covariates / confounders, a possible solution is to apply shrinkage / penalise all but the exposure(...
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Three continous variables + 2 factors vs. five continous variables to control for confounders?
I am trying to make sense of the design for my Master's thesis.
I am looking at how three different types of play relate to anxiety in children. So I have three continuous independent variables ...
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Understanding "Multiplication/ Group Operation" in Fractional Experiments
There's a group operation, at least of sorts, in Fractional Factorial design that I'm trying to understand. For definiteness, let's say we have 3 factors; A,B,C , at two levels each . Please critique ...
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Chi-Squared for demonstrating confounding in Logistic regression (or not...)
I am using logistic regression for inference and classification, using data from 190 X-rays/subjects. We want to see if certain X-ray measurements could predict development of a disease (Case vs ...
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When does Dose Response Function estimation work better than simple regression?
I have been recently asked what is the difference between a Dose Response Function (DRF) estimation (as the one proposed in this link and this paper) and a statistical regression method. I therefore ...
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IPW recalculating with deterministic treatment
Let's assume I want to calculate the ATE for a certain deterministic treatment, such as surgery (i.e., one either had it or not), and I'm interested in a per-protocol analysis.
Note that those who had ...
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How can I test whether a moderation effect is only present due to confounding variables?
I plan to investigate the effect of a personality trait on reaction measures (emotional reactions and intention for political participation) in a vignette study with two conditions. I assume that the ...
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How to account for confounders in a simple correlation analysis?
Beginner question sorry - I'm a coder and need stats advice.
I have a dataset broken down by local area, with columns for the proportion of owners who are French, the proportion of owners who grow ...
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A covariate as an inherent part of predictor
I want to compare brain volumes of two disease categories: young vs. old onset. I know that age, in general, is a covariate for brain volume. That is, the older the age, the smaller the brain. However,...
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Adding and interpreting covariates in logistic regression
I have a dataset and I want to do a logistic regression between the continuous variable "A" and the categorical variable "B". However, I also wanted to include "age" and &...
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Confounding variables in a t-test [duplicate]
Suppose you want to compare a certain score (dependent variables) in two groups A and B (independent variable), to see if one group has significantly better scores. You can run a t-test. Now what if ...
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Can I simulate an unobserved confounder to test sensitivity?
I have a linear regression model Y ~ b0 + b1*X, with X and Y as continously measured variables (say age -> income).
Assume I know from theory that there is an unobservable variable Z that is ...
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Why does controlling for a collider open a path, while controlling for a confounder closes a path, if there are relations to third variable for both?
Collider bias occurs when there is no association between X and Y but when a third variable which is caused by both X and Y is controlled for, this "opens a path" between X and Y and leads ...
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Logistic regression - When to include or exclude a confounding variable from the model?
I am working on determining if there is an association between a medication (yes/no) and a health outcome (yes/no).
However, the lines between what is a confounding variable to include in the model, ...
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Is it a problem to correlate X with changes in Y caused by X and Z?
Imagine you have three variables: X, Y, and Z. X and Z are both separate causes of Y.
You want to know if changes in Y caused by Z correlate with X. So, you manipulate Z to cause changes in Y. Let's ...
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Bias results for mismeasurement of continuous confounders
Consider data generated from a model $Y = \alpha A + \beta U$, where $U$ is a confounder, i.e. $\langle A,U\rangle \neq 0$. We don't measure U, but rather a noisy version of it, $U' = U+\epsilon$, ...
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How do random effects affect fixed effects (or just other coefficients) within a model
I have found that random effects terms can affect other coefficients within a model from here.
I see how in this example the coefficients change with the addition of a random effect; I'm still not ...
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Should You Specify a Curvilinear/Non-Linear Effect If You Suspect It is Spurious?
Consider the following (simplified) example of a project I am working on:
I assume that $X$ has a linear effect on $Y$. However, after plotting the relationship on a scatter plot, it looks like the ...
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Adjustment for confounders
I have some epidemiological data, relating to the prevalence of obesity at UK local authority level. For the purpose of exploration I want to derive the median obesity prevalence. However, I need to ...
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Confounding by indication in observational cohort study: how to address?
Background
We are conducting a register based cohort study into effect of pain killer medication in cancer patients. Index date is date of diagnosis of early cancer in a defined study population. We ...
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May unobserved variable confound or create open backdoor paths, why didn't controlling for the collider O make bad?
Is the U, the unobserved creating an open backdoor path or confounding?
Why condition on the collider Occupation good here?
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Comparing reduced, intermediate and complex model in logistic regression?
How should I compare my logistic regression models?
My study is about the association between diabetes (dia), and a genetic variation (GV) I'm adjusting for confounders such as gestational age (GA) ...
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Different results with splitting data vs. adjusting
I have a question regarding the results that I have achieved from my analysis. I'm new to statistics and the understanding of epidemiology. Please, help me interpret this better. I know what a ...
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Are we allowed to use classifiers to estimate the outcome in the first stage of Double Machine Learning when the outcome is binary?
It is clear to me how to proceed when the outcome is continuous, since the EconML and all other references I checked work with this type of examples (continuous outcome case). We simply apply a ...
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In my mixed effects model, are the confounded MCMC chains between my random intercepts and my global intercept problematic?
I implemented an MCMC algorithm for the following regression model:
$$y_i \sim N(\mathbf{x}_i'\boldsymbol{\beta} + \eta(\mathbf{s}_i) + \theta_i,\sigma^2),$$
$$\boldsymbol{\beta}\sim N(\boldsymbol{0},...
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Reporting effects of interest with and without a confounder: Is it reasonable?
Suppose that we have the following situation. With a predictor $X$ and a response variable $Y$, plus a confounder $C$, we consider two models: one is
$Y \sim N(\alpha_0+\beta_0 X, ~\sigma_0^2)$
and ...
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Controlling for a confounding variable in regression analysis
This question has come around due to a comment from a reviewer on a journal submission, but it has me interested and I want to see the general discussion on the subject.
I have a study where I'm ...
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Can confounders be controlled for in an Interrupted time series and when should outcomes be modeled as binary rather than aggregated rates?
I just learned about interrupted time series and have a few questions about them.
Say I have a dataset of individual patients and I want to compare their monthly rates of getting a certain lab test ...
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How can I control for confounding sociodemographic variables, in a correlational study with two IV's?
I want to control for the effects of confounds in an observational study (using self-report questionnaires), however, I do not want to imply a causal relationship between my two constructs. Therefore, ...
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Can selection bias lead to confounding bias?
I wonder if case-control matching will bring a new confounding bias into the matched design. In the following figure, $L$ is a confounder, $E$ is the exposure, D is the disease outcome. In the matched ...
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Is it a bad idea to use a variable that is strongly correlated with my independant variable of interest as a control variable?
I am currently looking into the correlation between academic freedom (my independant variable) and university rankings (my dependant variable) using OLS.
I find a negative significant correlation, but ...
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In regression, should we adjust for variables only associated with the independent or dependent variable?
I have recently been reading more about causal inference so am trying to conceptually think about model specification in more detail. From reading (e.g. this paper), we adjust for confounders which, ...
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Inclusion of three predictors that explain each other's variance
I would like to examine the relationship of a variable Y with a social construct.
There are three tests (A,B,C) that more or less characterise different aspects of the construct
(f.e., A= laboratory ...
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Do confounder variables need to have a causal effect on treatment AND response variables?
Let's look at an example:
We want to know if our new jogging shirt reduces the amount of sweat produced by runners. Ten factory employees in Bangkok, Thailand are recruited to try out the prototype ...
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In causal inference, can you control for confounders by matching the treatment and control group based on the time series of the outcome variable?
Suppose that Walmart has 100 stores. It has a coupon for cereal, and it wants to know if the coupon increases cereal sales by a significant amount.
Walmart puts the coupon on the cereal shelf in 10 ...
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How to interpret the results from estimating the confounder-adjusted survival curves when running the adjustedCurves package?
I've begun working with estimating confounder-adjusted survival curves using the adjustedCurves package in R and I need help interpreting results. Image A at the ...
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How to define parameters in the adjustedCurves package for estimating confounder adjusted survival curves? [closed]
I'm trying out the adjustedCurves package in R for estimating confounder adjusted survival curves, and I'm starting with the standard "lung" dataset ...
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