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.
417 questions
3 votes
1 answer
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Confounding due to minimal covariate overlap in glmm
I'm analyzing an ecological dataset of nutrient concentrations (continuous) across seven stations (each station is nested within one of three sites). We also have ~60 samples from each station where ...
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2 votes
1 answer
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3 votes
1 answer
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Can we retroactively infer the existence of an unmeasured motivation confounder in a pre/post case-control study? [duplicate]
Question concerning unmeasured confounders in a case-control healthcare intervention observational study comparing pre to post-treatment outcomes: After controlling for known confounders, if a member ...
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1 vote
1 answer
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Creating a synthetic control group with pre/post outcome data
I'm evaluating a pre/post case-control healthcare study with observational data where only a small percentage of eligible members in the treatment (intervention) group elected to participate. The goal ...
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1 vote
0 answers
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Include Time as a independent variable in the model for an ecological study
I am conducting my master's thesis on the temporal patterns of spore production in two specific species and the environmental drivers associated with these patterns. I began with a visual analysis of ...
0 votes
0 answers
48 views
How to treat confounding models
I am hoping to get an answer for this happening in my work (medical field). We have a very talented bunch of data scientists working on a XG Boost model for a while. One fine day they started using a ...
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2 votes
1 answer
155 views
Confounders: correlation or causation?
In the book Multivariable Analysis, the author states that "[A confounder is] associated with the risk factor and [is] causally related to the outcome" (pg. 6). Can this definition be ...
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0 votes
0 answers
30 views
Fixed effect and random effect in glmm are confounding
Hej, I am working on a dataset of fish sampled from different locations. There are two levels two the location: Stream is hierachically higher. Stretch is situated within Stream. I want to find out ...
4 votes
2 answers
100 views
How does baseline randomisation in an RCT address potential bias from time-varying confounders arising post-randomisation?
In the context of an RCT measuring outcomes over time (e.g., 1 year), external factors (potential time-varying confounders) can arise after randomization. My understanding is that baseline ...
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1 vote
1 answer
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difference between post-baseline confounders and intercurrent events (from estimand framework)
What is the difference between post-baseline confounders and intercurrent events in relation to the estimand framework? Specifically, using the example of rescue medication, how can it be classified ...
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0 votes
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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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0 votes
1 answer
83 views
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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2 votes
3 answers
134 views
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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2 votes
1 answer
78 views
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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2 votes
2 answers
343 views
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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