RE: order of covariate inclusion -> avoiding stepwise a pproaches -> abandoning exploratory analysis?

From: GUZY@xoma.com Subject: RE: [NMusers] order of covariate inclusion -> avoiding stepwise a pproaches -> abandoning exploratory analysis? Date: 9/29/2003 12:22 PM I like the "shoe size" example for covariate. I would like to add my part in this interesting discussion. I am a statistician and therefore aware of how statistics can erroneously lead us to no sense results if we do not pay attention. Suppose we have 100 covariate and let say that there is a 5% chance to get by chance a covariate which exhibit high correlation (we would therefore take it as a part of our model). In average we would have 5 covariate with high correlation that are the result of pure chance. Suppose now that we have 5 other covariate that really exhibit a real correlation and they show up when performing the analysis. If we don't do any statistical adjustment, we will take the 10 covariate and one of them would be for example shoe size. How can we know that shoe size was the result of chance while age for example was the result of a real trend when they both statistically exhibit the same strength of association? Now if you have 100 covariate, you must make a statistical adjustment to prevent inflation of the alfa type error and you can easily reach the situation where you will not be able to accept any of the covariate(you lost completely statistical power). Using purely a machine that would do everything for you without using any prior information (does not mean Bayesian) is not reasonable. I believe that experts should discuss the problem in advance and come up with up to 5 covariate maximum and then performing the analysis. The other issues related to the use of high number of covariate has been addressed before and I will not reiterate (collinearity, other numerical difficulty etc..) Serge Head of Preclinical Statistics and Pharmacometrics, XOMA Corporation President POP-PHARM