Dear Statalist,
We have been facing a truly terrible reviewer in one manuscript about obesity, nutrient intake and use of antidiabetic drugs.
The bottom line is: one of the reviewers wants an additional "test", which I have no idea how to perform. Even though our manuscript is relatively robust with interaction terms and evidence that more complex models do not fit the data better than simpler models (substantiated via LR-tests), the reviewer wants a "test" between estimates of the same coefficient in correlated models. The main idea (I guess) is given in the example below.
regress y drug a
regress y drug a b
Typically, I compare different coefficients, say, test a==b, but the idea is (somehow) test drug==drug.
Doest it make sense to compare the coefficient for drug (model 1) to drug (model 2) - considering the same subjects? Everything is equal, but covariate b, which is lacking in model 1.
Any thoughts?
All the best,
Tiago
We have been facing a truly terrible reviewer in one manuscript about obesity, nutrient intake and use of antidiabetic drugs.
The bottom line is: one of the reviewers wants an additional "test", which I have no idea how to perform. Even though our manuscript is relatively robust with interaction terms and evidence that more complex models do not fit the data better than simpler models (substantiated via LR-tests), the reviewer wants a "test" between estimates of the same coefficient in correlated models. The main idea (I guess) is given in the example below.
Code:
set obs 1000 gene y = rnormal(100,10) gene drug = runiform()>0.7 gene a = runiform() gene b = runiform()
regress y drug a b
Typically, I compare different coefficients, say, test a==b, but the idea is (somehow) test drug==drug.
Doest it make sense to compare the coefficient for drug (model 1) to drug (model 2) - considering the same subjects? Everything is equal, but covariate b, which is lacking in model 1.
Any thoughts?
All the best,
Tiago
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