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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 can I interpret relative and absolute income of both partners in one regression?

Suppose you want to examine the effect of income on the amount of housework for women. Does it make sense to include both relative income (compared to partners income) and absolute income of BOTH ...
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Combining Results of Simulation Replications (Random-Intercept Logit Models under Confounding)

I've written some simulation code in R to learn about the behavior of a random-intercepts logit model under varying degrees of confounding. The simulated scenario is three points in time, two groups, ...
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Variable as confounding if it influences other factors in opposite directions?

I examine the relationship between population density (PD) and the insurance density (ID) taking into account different market exploitations (ME) of an insurance company in municipalities. The ...
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Controlling for confounding variables

I have a dataset where some variables need to be controlled for body size and seasonal variation. There is a paper which describes controlling for skeletal size by using the residuals from a linear ...
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How to remove pair-wise comparisons that are affected by a batch covariate in pairwise Wilcoxon test

I have a dependent variable that is grouped by an independent.variable (with 3 factor levels) and I want to calculate pairwise comparisons between the groups of the independent.variable. However, I ...
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Propensity score matching: covariate balance

I have one concern about propensity score matching's assumption. It seems that what propensity score is doing is to say that the choice of treatment depends on pre-treatment covariates. Suppose I am ...
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64 views

How to test whether E[X]>E[Y] controlling for Z?

Question in mathematical terms. Assume an observation consists of three continuous variables $X$, $Y$ and $Z$. The sample comprises a sufficiently large number of observations. I would like to check ...
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38 views

If A causes B & A causes C, can B modify the effect of A on C?

Suppose I roll out an initiative to promote a new vaccine in a country, call this intervention A. A causes uptake of vaccines C, but it may or may not also cause backlash from community leaders ...
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Recovering hidden confounder in Simpson's paradox trends

I just watched a video of an interested talk from PyData LA: "Using Simpson’s Paradox to Discover Interesting Patterns in..." - Nazanin Alipourfard, Peter Fennell (https://www.youtube.com/watch?v=...
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How to Analyze A Confounded Design in R?

I have a $2^5$ factorial experiment, with one replicate, that is divided into $4$ blocks such that treatments $ACDE$ and $BCD$ (and $ABE$ as a result) are confounded with the blocks. I understand what ...
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Bias of omitting squares and interactions

With $x_1\sim N(\mu,\sigma^2)$ and a population model... $Y=\alpha_0+\alpha_1X_1+\alpha_2X_1^2+\epsilon$ ...if I run OLS omitting the square term... $y_i=\beta_0+\beta_1x_{1,i}+u_i$ ...the $x_1$ ...
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Should one control for non-confounders?

The standard error of the variable of interest $x$ can be calculated as $$s.e.({\hat\beta_x})=\sqrt{VIF_x\frac{\sigma_\varepsilon^2}{nVar(x)}} $$ As usual, $\sigma_\varepsilon^2=\sum_i\varepsilon_i^...
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Test for confounding variable S&P 500 Python

I'm looking into a possible topic for a school project currently. It involves looking at the S&P 500 in comparison to other indices globally (e.g., Nikkei, DAX, etc.). I currently have plotted 19 ...
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Standardization

what is the advantage of presenting area-standardised rate instead of crude rate when comparing different countries? Would the answer be that it helps to control for confounding (where area is a ...
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adjusting for a binary confounder of a continuous predictor in a glm

I would like to predict the chance of receiving a blood transfusion based on hemoglobin level of a patient (hemoglobin continuous, blood transfusion categorical). I found that patients with low ...
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ANCOVA multiple confounding continuous variables

I want to perform an ANCOVA analysis to test whether mpg shows differences between a categorical variable am; together I want ...
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Evaluating Impact of Unobserved Confounders - Is the E-Value applicable for Non-Significant Group Differences?

I have conducted an analysis of treatment effects based on observational data (via statistical matching). As suggested by VanderWeele and Ding (2017), I want to evaluate the sensitivity of my analysis ...
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Is correlation a sufficient remedy for inherently problematic designs?

I stumbled upon a research trying to decide whether an advanced version of a meditation is more effective than its basic version. For simplicity, let's call the advanced version A and the basic ...
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Forcing regression coefficients to be certain values based on population estimates

I'm working with a researcher who found this paper and suggested we do something similar rather than the proportional hazards model I suggested. The model used in the paper is what the authors call ...
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Avoiding adjustments for time-varying controls in difference-in-differences (DID)?

In difference-in-differences (DID) analysis, it seems like a "folk theorem" that one should be very wary of adjusting for time-varying controls. The reason, eminently plausible, is that time-varying ...
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Which approach based on the LASSO yields more biologically relevant results for gene data-sets?

I have a data-set with a continuous outcome variable and some confounding variables (like age, gender, ...) and many gene expressions (more than samples). The goal is to find relevant genes in ...
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Prove significant effect of third variable on a correlation

So I was measuring correlations in Boxscores of basketball players in the NBA. 3PA DRB -0.205499 I was trying to find some interesting correlations. ...
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Covariance between nominal and continuous variables?

I have an nominal independent variable (two groups) and a continuous dependent variable. A two-tailed Mann-Whitney test yields a significant difference between the two groups (p < 0.03; my data is ...
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How to test for confounds [closed]

I found some interesting correlations in my data. I believe that it might be caused by a confounding variable. How do I test for a confounding variable. Is it enough for a variable to correlate with ...
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How to remove the effect of confounding variables in sparse linear models?

I would like to build sparse linear regression model, but I would like to remove the effect of (hidden) confounding variables that control the input features and output variables jointly. I was ...
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normalizing individual patient reactions to medications in a study

We're trying to launch a study that looks at requirement of pressers, which vasoconstrict, for ICU patients with physical interventions to raise pressure, but some of these patients are under sedation,...
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result of matching analysis in confounding case

currently I'm studying Observation Analysis in my college. One of the part study explain about matching analysis in confounding case. In here, they told me that when we doing matched analysis, ...
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Matching by or adjusting for confounders?

When using regression models with a binary exposure, how do you choose whether to adjust for a confounders as covariates or to match the two exposure groups according to the confounders and then ...
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Main effect not significant, but confound is

I am a little bit stuck with my data analysis. What does it mean if your main effect is not significant (.442) but becomes even less significant when you add your control variable (.718) Furthermore,...
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multiple linear regression, confounding, group level predictors

I'm investigating the influence of several independent variables (IVs) (measured on the party,district level and individual level) on individual level campaign behaviour of ordinary candidates (index ...
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Adjusting simultaneously for multiple confounders

I am having trouble understanding how to adjust for confounders in a basic statistical analysis. As a simple example, assume I want to know whether the weight of group of sick subjects and a group of ...
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184 views

principal component analysis adjusting for confounder

Suppose I have a dataset of $n$ samples and $p$ variables ($V_1,V_2, ...,V_p$). The samples are from two groups ($G$) and $Z$ is a confounder. I would like to perform principal component analysis (PCA)...
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46 views

Interpretation of regression results from cross-sectional study after changing the position of outcome, predictor and covariate

I have a set of cross-sectional data have variables: Y (the expected outcome), X (the desire predictor), age, sex and Z (some confounding factors for example). Originally, I did a regression on Y ~ X ...
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181 views

“Matching” with cross-sectional studies: are the samples dependent?

I am comparing the prevalence of disease in participants of two separate cross-sectional surveys. One survey is from a group of prisoners and the other is from the general population. To account for ...
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How to standardize time-series data when looking for extremes

I have time series data for several groups of animals (birds, mammals, reptiles etc) which shows speciation rates over time. I want to see the effect of mass extinction events on these rates but I ...
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259 views

Age and sex are the most common confounders

I'm trying to understand confounders and I read the statement 'Age and sex are the most common confounders.' Can someone explain why this is? I don't fully understand the concept of a confounder to ...
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1answer
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regressing out confounders from negative binomial data

I have a very large dataset of count data. I know that these count data are discrete and follow a negative binomial distribution. I want to control the effects of two confounders. To do this, I plan ...
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How would you explain the relative importance of exploratory correlations for multiple regression models?

I am asking this from a pedagogic perspective:  ¿How do others explain (to their students or clients) the need to examine correlations between subsets of variables in multiple regression (MR) ...
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Can I report 'adjusted' cumulative incidence curves for competing risks survival analysis?

I am working on a cause-specific competing risks analysis looking at the association between factor X and specific causes of death such as cancer and CVD mortality. Correct me if I am wrong and I ...
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Is there a name for disingenuous correlation?

Just heard on on this segment of a Fox News show hosted by Tucker Carlson that in recent years gun ownership has gone up while gun-related violence has gone down. Just like that. Followed by the ...
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Removing confounding variable when comparing distributions

I have the following situation and am hoping that someone can point me in the right direction. I am completing an experiment under 2 conditions (1 and 2) and measuring 2 variables (A and B). I am ...
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Remove a dependency of a predictor after the model has been fitted (regression)

I have a question. I want to simulate some data. Let's say I want to predict salary and I have two predictor variables (origin and age). Furthermore, let's assume I have two different companies that I ...
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How to include day as a factor when you have to check different effect of different treatments also at the same time?

I have to check three levels of nutrition effect in combination to two levels of pH effect on the release of a chemical from the plants. Since we have done our treatments on different days. I would ...
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1answer
133 views

Are there any circumstances where adjusting for path variables / mediators is useful?

I’ve been taught that one of the criteria for being a “confounding” variable is that it should not be a path variable / mediator (ie. not a descendant of, or associated with, the exposure, and is ...
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Meaning of “shared familial confounding” in a study

From a news report: Using population-based, longitudinal cohort study data from Sweden, researchers examined the relationship of infection during childhood with IQ and adult nonaffective psychosis....
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Comparing different ways and advantages/disadvantages of summarizing/clustering and matching a set of explanatory variables?

I have the following data. Feature 1... Feature 15 is a set of explanatory variables. I have a group variable Med_group to show the two different possible medication received by patients. And then a ...
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Appropriate use of ANCOVA considering assumption of factor / covariate independence

I am trying to get my head around the implications of the assumption of factor/covariate independence and what appropriate uses of ANCOVA are. Several posts (e.g. here, here, and here) have already ...
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Teaching examples for internal validity: published experiments later shown to be confounded in an interesting way

There are some good examples of interesting/fun spurious correlations here (Examples for teaching: Correlation does not mean causation). Many/most of these are "funny" examples that aren't likely to ...
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How to perform adjustment for variables to adjust for confounding?

I'm starting Biostatistics beginning this spring semester and was reading some journal articles. I found this article http://www.sciencedirect.com/science/article/pii/S0085253815499211 at ...
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Model selection with fixed adjusting group of covariates

I am wondering if there exists any method to perform variable selection in the case where you want to adjust for confounding effects (whether or not they are significant). The setting is the ...