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Coleen B

Correct me if I am wrong but I think an assumption of random incidence would be wrong in these cases (perhaps a carry over from throw ones hands in the air epidemiological modeling) and stratification is needed. The only question is with which variables. We come across this frequently, with the main problem being the achievement of projectable sample size. Better to be partly right though....

Kaiser

CB: Yep, the whole enterprise of observational studies is to make adjustments and correct biases. Not adjusting at all is to allow the biases to fester. I think a good way of putting it is "we are confident the answer is partly right if our adjustments capture the key biases". Also, what did the researchers do to check their confirmation bias at the door?

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Kaiser Fung. Business analytics and data visualization expert. Author and Speaker.
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