Age and sex are the most common confounders I'm trying to understand confounders and I read the statement 
'Age and sex are the most common confounders.'
Can someone explain why this is? I don't fully understand the concept of a confounder to be honest.
 A: I don't think that statement makes any sense in isolation. 
A "correcter" way of saying it: "Age and sex are the most commonly adjusted for confounders in multivariate analyses". I will admit that's true, but the sense of necessity or sufficiency to address confounding that way is unwarranted. 
When we want to set up a causal model between an exposure and an outcome in observational research, it's important to consider the influence of confounders: these are things that are causal of the outcome and exposure such that, when ignoring their influence, the modeled association is different from the one that conditions on them. 
The challenges in identifying confounders in observational research:


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*They have to be easily measurable. Observational research is limited in the set of measures it can collect.

*You never actually know if something is a confounder.

*It's always a valid criticism that a proposed set of confounders don't satisfy the backdoor criterion.


For all these reasons, most causal analyses from observational data inevitably look back to age and sex as adjustments.
Two examples where age and sex don't confound anything:


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*We might have genotype data on SNPs in a case-control study of cancer. Neither age nor sex can possibly confound genetics. They can possibly interact in epigenetic ways, but that's moot (you need do to GxE analyses).

*We might have a cohort of monozygotic twins who are discordant for a disease and measured for exposure status. The conditional analysis has 0 intracluster variability in sex and age: they are identical genetically, born within minutes of each other. 


And yet, I have had reviewers suggest age and sex should be adjusted for in those scenarios. You have to laugh after a point...
