Hello,
Thank you in advance.
I have run some analyses on my data using quantile
regression (essentially, quite similar to linear regression, except that
it uses least absolute values to calculate the medians rather than means.)
Suppose I have a categorical variable in my models (e.g. female versus
male), and in the regression models, the coefficients are not
significant (i.e. Female gender is not significantly associated with
worse outcomes compared to males, p-value is greater than 0.05).
Now, I then ran the predict command to give me the adjusted values for
this regression model. When I determine the 95% confidence intervals of
the median for these predicted values, I find that the 95% confidence
intervals do not overlap.
First off, am I correct to assume that the 95% confidence intervals
should overlap if the predicted medians are not significantly different?
Or can a non-significant variable have 95% confidence intervals that
don't overlap (i.e. even though gender was not significant, the 95% confidence intervals can still NOT overlap)?
If I am doing things wrong completely, then please correct me!
---------------
What I did:
qreg [outcome variable] [predictor 1, e.g. female versus male]
[predictor2] [predictor3] // this was my quantile regression model
including my covariates
predict, xb // this is the linear prediction command that gave me my
adjusted values for the outcome variable
by [predictor1], sort: centile xb // this gave the median and 95% CI for
my adjusted values for the outcome variable, sorted by predictor 1 (e.g.
by female versus male)
Interpretation:
the qreg command gave me the preregression model and coefficients.
Assume that predictor1 (e.g. female versus male) was not significant.
the predict command gives me the adjusted medians.
Is this the correct way to obtain adjusted values according to my
regression model, and the correct way to obtain 95% CI?
Thank you immensely!
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