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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 adjust continuous and categorical variables for categorical variable?

I am performing a metaanalysis where I am trying to find predictors for an ordinal response variable. Additionally, I want to perform pair-wise correlations on some of the variables. I have a ...
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“marginal standardisation” for comparing predicted probabilities between two groups? [on hold]

I have a doubt regarding to the R package "margins". I'm estimating a logistic model: ...
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Control for baseline incidence of disease in Kaplan-Meier curve

I would like to construct a survival curve from retrospectively gathered data that represents the time to onset ($t$) of disease ($d$) after some specific event ($x$). We know that $x$ predisposes to ...
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What is function of Holm-Bonferroni? [duplicate]

I am confused about Holm-Bonferroni. Suppose we want to compare age of female and male students using t tset. We also want to compare length of arm and elbow between male and female students. I ...
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Year of intervention seems a better predictor than type of intervention (which is dependent on the year itself)

We have a small(n = 19) non-randomized pilot clinical study in which we compare two types of surgical procedures on various outcomes. The choice of which procedure was to be performed depended ...
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1answer
39 views

What type of regression for two groups of data?

I have data from 500 school children who took a test. 250 of the children have a certain type of disability (group A). Each child in group A was matched to a child on the basis of age, gender to a ...
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18 views

Difference between Confounder, Mediator, Precision Variable, Effect Modifier

While I do have the definitions for each of these 'memorized', I find it hard to actually distinguish between them during the initial steps of confirmatory data analysis. Example Looking at how ...
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26 views

How to determine which independent variables to add in a multivariable regression model when sample size is small

As a post-doc I am working on some data that I did not collect myself. The central question I'm trying to answer: is there a difference in the fat mass of neonates born to mothers with gestational ...
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controlling confounding variables vs. excluding confounding variables

I'm working on a meta-analysis project that looks at the effect of "pure" depression (i.e., depression with no anxiety) on mortality. For studies that looked at the effect of pure depression on ...
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The effect of a lack of dose-response results on odds ratio

This is part of a quantitative reasoning assignment I was working on. The study hypothesized that exercise may reduce the risk of disability for activities of daily living(ADL). However after ...
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Deriving the values of the range around the mean value

This is part of a bigger quantitative reasoning assignment I was working on. My understanding here is that the upper bound and lower bound of the ranges for each of the exercises should be reflected ...
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1answer
34 views

Could confounding factors unmask a significant relationship?

Suppose we have one dichotomous dependent variable and ten independent variables ( mix of categorical and continuous), by using a $t$ test and a $\chi^{2}$ test no relation was found between each ...
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24 views

How to interpret OR confidence intervals for interaction or confounding?

This is a textbook problem I've been trying to understand I wanted to check if my thinking is correct (unfortunately there is no solution manual) I have a case-control study where I'm looking at ...
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11 views

discover a latent (confounding) effect in a model

Lets say I want to model the relationship between sales and 3 predictors: marketing, competition and visits (mkt, ...
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Do zero-inflated models induce selection bias?

Zero-inflated models (e.g., ZI poisson, ZI negative binomial, hurdle) assume two processes for the generation of the observed outcome variable: a process for deciding whether the outcome is zero or ...
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43 views

Confidence interval for Population Attributable Fraction with several strata

I have used aggregated data to create a table of person-years (pys) and deaths by social class, age and sex. If we consider social class to be a modifiable factor, we can calculate the number of '...
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48 views

Calculating confidence intervals for “excess events”

I have a study that shows 100 deaths occurred in a cohort of high-risk people. Given mortality rates in the general population, you would only expect 70 deaths to occur. The standardised mortality ...
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102 views

What is the difference between covariate and confounding variables?

What do covariate and confounding variables have in common and how do they differ? And what are their specific effects in causal inference? (in statistics and causal inference)
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Effect Modification, Confounding, and Correlation

I am trying to estimate the effect of a policy intervention (X) on patient outcome(Y) from an administrative data. In the data set, around 20% of the patients have received the intervention. Even ...
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2answers
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How to test if a variable is a confounder in a repeated-measures design?

I would like to test is a given variable is associated with both the dependent and the independent variables (and therefore a potential confounder) in a repeated-measures design. My model has a ...
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Confounding variables in experimental study

We conducted a study to analyse the effect of tablet named 'xab' that help smokers to stop smoking. 5500 of smokers are selected. half of them were given different doses of tablet while the other ...
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Analyse interaction/moderators first and then adjust for confounding

In my research I am interested in subgroup analyses. I am looking at a general association between two variables (environment and health outcome) with interaction terms (personal factors, which are ...
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55 views

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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183 views

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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19 views

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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25 views

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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1answer
44 views

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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1answer
81 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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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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37 views

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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51 views

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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29 views

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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1answer
97 views

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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30 views

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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36 views

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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38 views

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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38 views

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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1answer
25 views

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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116 views

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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1answer
19 views

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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1answer
103 views

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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2answers
76 views

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,...