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Re: st: Verify randomization in a large sample
I agree with Kieran that imbalances are to be expected and that they
don't necessarily indicate any kind of bias. Multiple, large imbalances,
however, likely mean that the allocation process was off in some way and
should be investigated. (And I mean investigated by talking to the
people who did the allocation and looking at their programs, not by
doing more statistics.) You may not be able to redo the allocation, but
if something's wrong, surely you would want to know why.
-ml
Kieran McCaul wrote:
If the purpose is to check "balance" after randomization, I can't see how any statistical testing will help.
Statistical tests test a null hypothesis against an alternative.
The null is essentially "any differences are no greater than would be expected by chance alone'. The alternative is "differences are so large that they are unlikely to be due to chance".
If the study has demonstrably been randomized, then all differences, no matter how extreme, are due to chance.
Lack of balance, which some people seem to obsess about, is not an indication of failure of the randomization process. Lack of balance will occur. It will occur. Always.
The purpose of randomisation is to remove bias, not achieve balance.
Lack of balance will be a problem if it biases comparison between arms of the study. So adjust for the lack of balance in the analysis.
______________________________________________
Kieran McCaul MPH PhD
WA Centre for Health & Ageing (M573)
University of Western Australia
Level 6, Ainslie House
48 Murray St
Perth 6000
Phone: (08) 9224-2140
Fax: (08) 9224 8009
email: [email protected]
http://myprofile.cos.com/mccaul
_______________________________________________
The fact that no one understands you doesn't make you an artist.
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Austin Nichols
Sent: Wednesday, 1 October 2008 10:05 AM
To: [email protected]
Subject: Re: st: Verify randomization in a large sample
Jos� Luis Ch�vez Calva <[email protected]>:
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, Jos� Luis Ch�vez Calva
<[email protected]> 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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Michael I. Lichter, Ph.D.
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UB Department of Family Medicine / Primary Care Research Institute
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Office: CC 125 / Phone: 716-898-4751 / E-Mail: [email protected]
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