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Estimating peer effects with observational data is very difficult because of contextual confounding, peer selection, simultaneity bias, and measurement error, etc.
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Observational studies may overestimate treatment effects because of confounding; minimizing confounding is the principal reason for doing randomized trials.
In fully adjusted models, we also adjusted for contextual confounding by area-level mean income (yearly) and proportion of unemployed, using data from Statistics Sweden (http://www.scb.se/en_/).se/en_/
We tested numerous individual and contextual confounding variables.
This isn't about seeming to be disrespectful because of contextual disconnects.
We chose to pool the adjusted effect measures rather than the crude ones because of the role of confounding factors on the validity of observational studies.
Conclusions: The results appear to be because of a confounding effect rather than rehabilitation.
These data must be interpreted with caution because of several confounding factors.
The effect could be causal or because of uncontrolled confounding.
This, we suspect, is because of negative confounding.
Simultaneous statistical control of potential confounds would be informative because of conjoint confounding that stepwise approaches do not capture.
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