Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down on April 23, and its replacement, is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: Quantile regression

From   David Hoaglin <>
Subject   Re: st: Quantile regression
Date   Fri, 21 Sep 2012 22:43:12 -0400

Dear Vasan,

I'm puzzled.  From the way in which you described your analysis in
your first message, I don't understand why you would use quantile
regression.  As I recall, you wanted to compare the means of some
variables across quartiles of BMI for males and females.  In that
description, it was not clear to me whether you wanted to compare the
mean of a variable in data from males among the quartiles of BMI and
similarly in data from females, or whether you wanted to compare the
female mean and the male mean within each quartile of BMI, or whether
you wanted to make both of these types of comparisons.  I did not see
any mention of the numbers of observations or the source of the data
or, importantly, the scientific question that you are addressing.

As I read the command below, you are asking -qreg- the fit a
regression model to the median of BMI with predictors fast_glucose,
etc. (the median is the default quantile in -qreg-).  This seems far
from what you set out to do.

Those of us who are following this thread would be better able to
advise you if you went back to the beginning and gave us more
information on the data and the context.  I do not know, for example,
whether the data that you are analyzing are suitable for ANOVA.  They
may be (perhaps after a transformation), and you may have given up on
ANOVA too quickly.


David Hoaglin

On Wed, Sep 19, 2012 at 5:33 PM, Vasan Kandaswamy
<> wrote:
> Many thanks Nick.
> Now, I have given up on ANOVA since I cannot derive p values for gender seperately, but did a regression.
> A quantile regression this way comes up this way
> bysort bmi_q sex:sum g0mmol
> bysort sex: qreg bmi fast_glucose age pr ( adjusted for age)
> I tabulate the output this way
> BMI                Q1      Q2        Q3        Q4     Beta (95%CI)            P value
> Male              5.3     5.4        5.5        5.6     2.61 (1.46, 3.76)     8.91 x 10^-06
> Female         5.4      5.4       5.4         5.7    0.36 (-0.15, 0.86)     0.168
> IF you actually look at the mean glucose values in Q1-Q5, there is not much difference, but the regression shows a clear difference with p values of males significant, while females are not.
> Could you please explain of my approach is correct.
> The basic question I would like to ask is if the fold change from Q1 to Q5 is significant.
*   For searches and help try:

© Copyright 1996–2016 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index