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From |
"Michael I. Lichter" <mlichter@buffalo.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Verify randomization in a large sample |

Date |
Wed, 01 Oct 2008 16:09:01 -0400 |

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: kamccaul@meddent.uwa.edu.au

http://myprofile.cos.com/mccaul _______________________________________________

The fact that no one understands you doesn't make you an artist.

-----Original Message-----

From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Austin Nichols

Sent: Wednesday, 1 October 2008 10:05 AM

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.

* For searches and help try:

* http://www.stata.com/help.cgi?search

* http://www.stata.com/support/statalist/faq

* http://www.ats.ucla.edu/stat/stata/

*

* For searches and help try:

* http://www.stata.com/help.cgi?search

* http://www.stata.com/support/statalist/faq

* http://www.ats.ucla.edu/stat/stata/

-- Michael I. Lichter, Ph.D. Research Assistant Professor & NRSA Fellow UB Department of Family Medicine / Primary Care Research Institute UB Clinical Center, 462 Grider Street, Buffalo, NY 14215 Office: CC 125 / Phone: 716-898-4751 / E-Mail: mlichter@buffalo.edu * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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