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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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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 ...
jorvaor's user avatar
4 votes
1 answer
90 views

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(...
user167591's user avatar
3 votes
2 answers
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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 ...
Ksenia's user avatar
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5 votes
1 answer
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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 ...
MSIS's user avatar
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2 votes
0 answers
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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 ...
Maks Hall's user avatar
1 vote
1 answer
80 views

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 ...
DaSim's user avatar
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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 ...
Uri Gottlieb's user avatar
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1 answer
26 views

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 ...
al01's user avatar
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1 answer
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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 ...
Richard's user avatar
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2 votes
0 answers
32 views

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,...
user23253590's user avatar
4 votes
1 answer
236 views

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 &...
Erfan Naghavi's user avatar
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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 ...
Papagon's user avatar
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1 answer
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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 ...
JPK1778's user avatar
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4 votes
2 answers
239 views

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 ...
JElder's user avatar
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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, ...
learning_890's user avatar
1 vote
1 answer
28 views

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 ...
puzzleGuzzle's user avatar
5 votes
1 answer
61 views

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, $U' = U+\epsilon$, ...
user310374's user avatar
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59 views

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 ...
Geoff's user avatar
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7 votes
3 answers
637 views

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 ...
Brian Lookabaugh's user avatar
2 votes
0 answers
31 views

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 ...
j.rahilly_UCL's user avatar
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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 ...
user167591's user avatar
2 votes
1 answer
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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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If you condition on O, you stop the flow of information from which arrow into or out of O? [duplicate]

Why does conditioning on O open up this second channel below?
jkj's user avatar
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How to test for effect modification and mediation by time-varying factors in a longitudinal context?

I have longitudinal data with measures of an exposure, outcome, variables that may be mediators or effect modifiers, and confounders at 3 time points. I would like to account for time-invariant and ...
JRB's user avatar
  • 471
1 vote
1 answer
28 views

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) ...
Devi Sita's user avatar
  • 333
1 vote
1 answer
122 views

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 ...
Devi Sita's user avatar
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1 vote
0 answers
139 views

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 ...
Caio's user avatar
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4 votes
0 answers
18 views

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},...
Ron Snow's user avatar
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22 views

Panel vs. cross-section: different impact of a variable

I study the composition of the labor force, which consists of two groups of workers in a particular industry, full-time and part-time employees. I have data for two years, and each year I observe a ...
Mikhail's user avatar
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2 votes
0 answers
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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 ...
bluepole's user avatar
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4 votes
1 answer
78 views

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 ...
Rhys Maredudd Davies's user avatar
1 vote
0 answers
102 views

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 ...
M. Yates's user avatar
1 vote
1 answer
83 views

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, ...
Liam C's user avatar
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2 votes
0 answers
31 views

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 ...
Vincent's user avatar
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3 votes
1 answer
104 views

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 ...
Muller I. 's user avatar
3 votes
1 answer
104 views

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, ...
Sam's user avatar
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1 vote
0 answers
18 views

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 ...
a.henrietty's user avatar
1 vote
1 answer
38 views

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 ...
Cotton Headed Ninnymuggins's user avatar
1 vote
1 answer
136 views

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 ...
Iterator516's user avatar
1 vote
2 answers
234 views

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 ...
Village.Idyot's user avatar
2 votes
1 answer
241 views

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 ...
Village.Idyot's user avatar
1 vote
1 answer
66 views

Logistic regression and confounders or mediators

For a researchproject I would like to use the logistic regression model. I look at the relationship between SES (categorial) and participation in training (dichotomous). Now there are several ...
user383942's user avatar
5 votes
1 answer
53 views

How to adjust for the confounder of a confounder and how to call the confounder of a confounder within treatment effect estimation?

How do we adjust for the confounder of a confounder in order to compute unbiased estimates of the treatment effect of $A$ on $D$? See the causal graph (DAG) below: What do we call the confounder $C$ (...
CausalQuestions's user avatar
1 vote
0 answers
30 views

Adjusting for mismeasured confounder

Suppose I would like to determine the causal effect of $X$ on $Y$, where the relationship is confounded by $U$, but I measure $W$, which is a mismeasured proxy of $U$, as in this paper. That is, $$U \...
user310374's user avatar
0 votes
0 answers
111 views

Compare two groups with differnt treatments and different populations

I will simplify my research question. I have 40 patients (group 1) with assessed a variable v1 (e.g. their blood preassure) at timepoint 0 ( v1(t0) ). After treatment (treatment 1 at timepoint 1) I ...
burazija's user avatar
2 votes
1 answer
95 views

How control for a pre-treatment outcome $Y_0$ if is a strong confounder while avoiding regression to the mean bias for treatment effect on $Y_1$?

I'm facing a dilemma in a pre/post cohort matching analysis for a healthcare intervention: Matching on the pre-treatment outcome $Y_0$ (a continuous variable) will likely lead to regression to the ...
RobertF's user avatar
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0 votes
0 answers
45 views

Antimalarials as confounding variables in a prevalence study

I am performing a multi-level logistic regression analysis of cross-sectional survey data, and I am curious as to whether "taking antimalarials" is a confounding variable in the following ...
Trypanosoma's user avatar
1 vote
0 answers
15 views

Is a high prediction performance of allocated treatment evidence for lack of unobserved confounding?

Example Say Investigator 1 is looking to investigate the effect of fish consumption (a binary variable $A$ indicating whether they eat or do not eat fish) on cardiovascular health (a continuous ...
Kuku's user avatar
  • 1,471
2 votes
1 answer
464 views

Wilcoxon signed-rank test with an additional covariate

Problem formulation I have 5 strains on which I administered treatment and repeated the experiment two times independently. So my data looks something like this: ...
chickenNinja123's user avatar
1 vote
0 answers
17 views

Members in treatment group serve as controls for other treatment members to eliminate unmeasured confounders in pre/post observational study?

Consider a typical observational healthcare study where pre/post health outcomes among patients receiving a treatment or care program are compared to a counterfactual control group who didn't receive ...
RobertF's user avatar
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