Its true that a specific maximum number can not be given, but I would like to know the possible implications of increasing the number of confounding variables. What factors would limit the number of confounds to be included?
There are a number of issues:
1. With too many variables, you run the risk of over-fitting, which is why we have rules of thumb about the number of subjects required per parameter in the model;
2. Some of the potential confounding variables may be what is known as colliders, and you can even introduce bias by including them in the model. Directed acyclic graphs (DAGs) can help sort this out;
3. By using treatment effect techniques such as propensity score matching and inverse probability weighting, we can remove the need to include a large number of confounders in a model.
Confounding variables (aka third variables) are variables that the researcher failed to control, or eliminate, damaging the internal validity of an experiment.A reduction in the potential for the occurrence and effect of confounding factors can be obtained by increasing the types and numbers of comparisons performed in an analysis.
It is advisable to have minimum nos. of confounding variables as possible.
Need to consider the appropriate adjustment sets required - I recommend starting with a system such as Textor's DAGitty: http://www.dagitty.net/ - and the ability to obtain the necessary data on an appropriate number of subjects for all required adjustment sets.
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