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.

learn more… | top users | synonyms

0
votes
1answer
51 views

Ignore strata in external validation of stratified Cox prop hazards model?

I've fit a stratified Cox proportional hazards model to some survival data, where I've stratified by a potential confounder which is the batch the data comes from (there are three batches). Now, I'd ...
0
votes
0answers
15 views

Confounding vs. effect modification in 2x2 tables

I'm doing a statistics course at the moment and have a quick question about effect modification and confounding, and working out which is which in 2x2 tables in a case-control stud. I have three ...
0
votes
0answers
20 views

Multilevel propensity score

I'm trying to analyze many treatments on outcome after propensity score 1:1 matching. My problem: I have 6 differents drugs and each patient can take or not each of these. If I build my propensity ...
1
vote
0answers
17 views

Help with real examples of non-confounders

I am looking for some real clinical examples for variables that are NOT confounders: 1) variables only impact treatment, but not outcome. 2) variables only impact outcome, but not treatment So the ...
0
votes
0answers
10 views

Is this a confounding interaction: using demographic data in a fractional factorial design?

I've created and run a choice experiment (conjoint analysis) using a fractional factorial design 3 x 3 x 3 x 3 (four factors with three levels each). I also collected some demographic data (age, ...
1
vote
1answer
45 views

Statistical issues with aggregating annual survey data from multiple years?

I am using a national telephone survey conducted every year by the CDC called the Behavioral Risk Factor Surveillance System (BRFSS) to answer a question about breast cancer screening rate in ...
0
votes
0answers
13 views

Can I examine the confounding effect of variables on non-normal data using Pearson?

I have used Kendall's tau to examine whether there is a correlation between a number categorical variables, as I have a small sample. However, I also want to test whether some variables might have an ...
0
votes
0answers
14 views

Find relations without confounding variables? [duplicate]

I have multiple numerical and categorical variables which I'd like to data-mine for simple relations. I'd create simple plots of two variables which are supposed to have a meaningful statement. Can ...
0
votes
0answers
17 views

counfounded batch effects in microarray dataset - can I do partial experiment redesign?

I'm working with a microarray data set where the batches are completely confounded treatment time, i.e. time t1 is all in batches b1 and b2, and time t2 is all in batches b3, b4, and b5. I know this ...
1
vote
1answer
13 views

Find the effect of a attribute value on an outcome by eliminating confounding values

I have a series of lets say five attributes. The first attribute is called diagnosis code 1, the second diagnosis code 2 etc. The values are codes which represent diseases. In other words, each ...
0
votes
0answers
14 views

Mixture model for dependent observations with additive group-level confounders

I'm looking for a special type of mixture model (described below) and I'm hoping to get some hints with regards to relevant literature to look at or names to be searching for. On the general level, ...
0
votes
0answers
12 views

Dropped cases from matched studies

We have cohort data and a rare exposure which we are matching to controls in a large epidemiologic dataset. The matching variable is a deidentified neighborhood indicator (cluster) which guarantees ...
2
votes
1answer
29 views

Issue with controlling confound in multinomial regression analysis; different results when removing kids on meds

I examined the influence of ADHD on abnormal bodyweight in a very large, national sample of children. In my multinomial regressions, I controlled for several specific confounds, which have been shown ...
1
vote
1answer
24 views

Alias Structure of one-fourth replicate of a $4^2$-Factorial Design with interaction $\text{A}{B}^3$ confounded

For finding the Alias of main effect A , i started as the following : $\text{A}$$\times$ $\text{A}{B}^3$$=\text{A}^2\text{B}^3={(\text{A}^2\text{B}^3})^2=\text{A}^4\text{B}^6=\text{B}^2$(mod ...
0
votes
0answers
26 views

Meta-analysis with known confounder

I’m performing a meta-analysis in which the main outcome of interest is a correlation coefficient between two variables, $X$ (a psychological measure) and $Y$ (a biomarker). $Y$ is known to be ...
3
votes
1answer
125 views

Infer causality with high collinearity

I recently started to ask myself how to measure the impact of education on indexes like GDP: what is the outcome of mathematics or computer science on GDP, at the country level for instance. In this ...
0
votes
0answers
70 views

Random forest: confounding factors

I have N variables in K samples. There is a classification variable, T (treatment), and a confounding variable -- sex. Unfortunately, in the "no treatment" (CTRL) group there are significantly more ...
2
votes
1answer
102 views

What is the purpose of precision variables?

Why do we need to include precision variables in a regression model (i.e., a variable that is associated with the outcome but not the predictor of interest)?
1
vote
3answers
143 views

Including confounders in a model

Suppose that you are performing a linear regression examining the main effect $x_1$ and want to adjust for possible confounders $x_2, x_3, x_4$. Is it better to have an unadjusted model and a model ...
1
vote
2answers
177 views

Multicollinearity when adding a confounding variable

When you run a regression on ice cream sales with predictor shark attacks, you find a significant coefficient. But that is because there is a confounding variable temperature. But how do you correct ...
2
votes
2answers
229 views

Whitening data before regression, should I whiten the response variable too?

I have some data X where the samples are not independent (they're correlated with each other), and I'm trying to do a regression of some continuous variable y on X. This sample correlation could ...
2
votes
1answer
91 views

Purposeful selection and confounding

I conducted purposeful selection as outlined in Jewell's Statistics for Epidemiology. The log likelihood tests showed covariates, which I considered to be confounding though not significant in the ...
5
votes
1answer
326 views

How to calculate permutations of categorical variables with R

I want to simulate or calculate probabilities of combinations of group membership for different sample sizes (e.g., n= 3, 4, 5, 10, or 100) for two groups (of the same sample size). Each outcome could ...
0
votes
0answers
28 views

Discounting effect of one variable on another variable

Hope I manage to explain this simply enough so I don't end up confusing myself and you along with me! I've measured a variable (rate of soil respiration) throughout the day. However my variable is ...
7
votes
2answers
394 views

What examples of lurking variables in controlled experiments are there in publications?

In this paper: Lurking Variables: Some Examples Brian L. Joiner The American Statistician Vol. 35, No. 4, Nov., 1981 227-233 Brian Joiner claims that "randomization is not a panacea". This is ...
0
votes
0answers
29 views

Group comparison: match on variable

I am comparing two groups (A and B). Group B is matched to group A on variable X, by sampling two subjects from a population for each subject in group A. Group A has ~200 subjects, group B has ~400 ...
1
vote
0answers
44 views

Dealing with perfectly confounded microarray experiment

I need to compare microarray data, where all of the "cases" were hybridized in one batch and all of the "controls" in another, so I have no way of removing this batch effect. What would be the best ...
1
vote
1answer
45 views

Case-control study with data collected in batches

I have (matched) case-control data. The data is collected in batches in such a way that the batch determines some quality of the data (there is a 'batch' effect). Also, the cases and controls were ...
3
votes
0answers
61 views

Confounder - definition

According to M. Katz in his book Multivariable analysis (Section 1.2, page 6), "A confounder is associated with the risk factor and causally related to the outcome." Why must the confounder be ...
2
votes
2answers
127 views

How can I convert my dataset into a new dataset which is adjusted for confounding covariates?

I have used software before to do linear regression and factor in/out the confounding variables, but what I would like to do is generate a new data set which is adjusted for the confounding variables. ...
4
votes
1answer
218 views

Hypothesis test on data with confounding spatial clustering

This is a bit of an elaboration on a question I posted earlier, since I feel like my approach to the problem as a whole is probably quite flawed. Suppose I have a set of treatment and control cells, ...
2
votes
1answer
187 views

Can ANCOVA disagree with multiple regression?

I have 3 categorical variables (CVa, CVb, CVc) all 0 or 1. Two continuous variables (IV1, IV2) are confounding my observational study. The multiple regression ...
5
votes
1answer
480 views

Does adjustement completely remove the effect of the confounding variables?

This might seem a silly question but I am really confused about it. In theory adjusting for a confounder variable should remove its effect. Is this always true? and does this mean that the effect of ...
0
votes
2answers
120 views

Visualizing association results after adjusting for confounders

I'm trying to find a way to visualize the results of an association analysis where I corrected for confounding variables. I have a set of cytokine data (amount of protein in the blood) from a set of ...
0
votes
2answers
264 views

Adjusting for confounding variables in binary response variables

I have a dataset of patient information and I'm looking to find a way to compare two groups of patients and take into account confounding variables. My dataset has an N of ~1500 and I'm looking for a ...
8
votes
1answer
149 views

Techniques for analyzing ratios

I am looking for advice and comments that deal with the analysis of ratios and rates. In the field in which I work analysis of ratios in particular is widespread but I have read a few papers that ...
1
vote
1answer
124 views

Can lack of main effect and lack of interaction be caused by the same confound?

Can the lack of main effect have the same underlying cause as the lack of interaction in 2-way ANOVA? My results failed to reach significance for variables of gender and language. Is it possible that ...
0
votes
1answer
101 views

Can a confounding variable be correlated with the DV and not the IV?

Can a confounding variable be correlated with the DV and not the IV? I have heard of the DV being corr. but I can't find IV in any textbooks. I found this def. in Wiki: ...
0
votes
0answers
40 views

Examples of a confounding variable [duplicate]

Possible Duplicate: Correlation does not mean causation What is your favorite example of a confounding variable / confounding effect?
7
votes
1answer
627 views

Is it possible to have a variable that acts as both an effect modifier and a confounder?

Is it possible to have a variable that acts as both an effect (measurement) modifier and a confounder for a given pair of risk-outcome associations? I'm still a little unsure of the distinction. I've ...
0
votes
0answers
101 views

Using z-standardization to account for covariate

I would like to know whether z-standardization is an appropriate way to account for a covariate. Please consider the following dummy example (I am not interested in the interpretation of the result ...
4
votes
3answers
170 views

Understanding regression results when data are subsetted

I have some data that span several years: 2006-2010. I have run logistic regression to model the data. For the whole dataset, I get a 95% confidence interval for the odds ratio of a parameter of ...
0
votes
1answer
347 views

Which test to use to check if a possible confounder impacts a 0 / 1 result?

I've been given a task with the following question: Investigate whether or not the type or treatment (0 or 1) has an impact on the result (0 or 1) The same as 1), but keeping in mind another ...
3
votes
2answers
104 views

Checking whether or not a variable has impact

For a statistics assignment, I've been given a data set (regarding drug prevention) and a few questions. One of the questions is to check whether or not the choice of treatment, treatment A or ...
2
votes
1answer
80 views

Account for group age differences

I have a dataset of two patient and one healthy control group which I would like to compare (using R) with respect to a continuous outcome variable (each subjects is measured once). However the groups ...
6
votes
3answers
198 views

What to conclude when you fail to find an association in an epidemiological study?

Normally when somebody finds an association in an epidemiological study people are quick to point out that it doesn't prove causality, that there are problems of missing co-founders, that it is at ...
1
vote
0answers
181 views

References about univariable vs multivariable variable selection

Suppose I have variables $X_j$, $j=1,\ldots,p$, some of which are correlated, and some continuous output $y$. I want to rank the variables by importance. One way is to do an association test of each ...
0
votes
1answer
186 views

Please help to understand Agresti's book

I need some help to understand some topics in Agresti's Categorical Data Analysis. In section 6.3.1 (p 231), he provided a model like: $$ \text{logit}(\pi_{ik})=\alpha+\beta x_i+\beta_k^Z $$ where ...
2
votes
1answer
175 views

Does control get us closer to or farther from causation?

In logistic regression with an N of 40,000, purchase decision is unrelated to price. However, with certain demographic variables controlled, price can show a positive coefficient of meaningful ...
16
votes
3answers
1k views

Basic Simpson's paradox

I have a question about something that my statistics teacher said about the following problem: There are two hospitals named Mercy and Hope in your town. You must choose one of these in which to ...