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From |
"Kieran McCaul" <kamccaul@meddent.uwa.edu.au> |

To |
<statalist@hsphsun2.harvard.edu> |

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

Date |
Wed, 1 Oct 2008 10:36:01 +0800 |

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/

**References**:**st: Verify randomization in a large sample***From:*"José Luis Chávez Calva" <josechc@gmail.com>

**Re: st: Verify randomization in a large sample***From:*"Austin Nichols" <austinnichols@gmail.com>

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