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RE: st: Verify randomization in a large sample


From   "Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Verify randomization in a large sample
Date   Wed, 1 Oct 2008 08:29:09 -0700

The tests Dr. Nichols notes assume normality - not much of an issue for univariate issue unless there is bad skewness.  The multivariate test based on Hotelling could be an issue as it isn't quite as robust to non-normality.

The testing of balance after randomization is often done in the pharmaceutical industry but Senn had a good article in Statistics in Medicine on this about 10 years ago.  It's not sensible, as all it does is verify if you did the job right, and if you didn't what then?

Others have suggested using the test to determine if you should adjust for the variables that aren't balanced.  This is allowing the data to determine the analysis, and is completely exploratory.  If you are planning to adjust for covariables, you should specify these a priori - and usually these are based on their potential effect on the response.

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Tony

Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Austin Nichols
Sent: Tuesday, September 30, 2008 7:05 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Verify randomization in a large sample

José Luis Chávez Calva <josechc@gmail.com>:
The only way to verify randomization is to observe the randomization
mechanism.  But you can check the balance by comparing means of
several variables in the dataset like age, gender, language, etc.
across categories.  For example, if you have treatment and control
groups defined by a variable t (=0 for control and =1 for treatment),
you can do
 hotelling age gender language etc, by(t)
or
 reg t age gender language etc
to get an F test of the null that all means are the same.  Assuming
variances may differ, you can
 reg t age gender language etc, r
and for alternative models you can run logit or probit instead (to get
a chi2 test).  Presumably, for a categorical t you could run
 mlogit t age gender language etc
or -mprobit- assuming a specific error distribution under the null of
randomization (in which case the X vars should not help you predict
t).  All of that is just for comparisons of means; for higher moments
you will need tests of equality of distributions (e.g. -ksmirnov-) or
graphical methods (e.g. -qqplot-).

On Tue, Sep 30, 2008 at 8:18 PM, José Luis Chávez Calva
<josechc@gmail.com> wrote:
> Dear Stata users:
>
> I have a dataset on household income with a large number of
> individuals. The set contains one variable indicating the locality
> where each individual lives and another one indicating the household
> to which this individual belongs to. I would like to know how to
> verify randomization both at locality and household level using
> several variables in the dataset like age, gender, language, etc.

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