RE: Centering (was Re: Missing covariates)

From: "Stephen Duffull" <sduffull@pharmacy.uq.edu.au> Subject: RE: Centering (was Re: Missing covariates) Date: Thu, 5 Jul 2001 13:09:34 +1000 Hi I have a question about "centering". As I understand centering - or what seems to be commonly referred to as "centring" in UK statistical texts - it is a method for reducing the influence of collinearity that arises due to computational processes (rather than some intrinsic feature of the variables). The act of centring (eg Xi-mean(X)) helps to eliminate this computational collinearity problem (a typical example of computational collinearity occurs when polynomials are used as models hence X and X^2 are likely to be correlated). Centring will not help for intrinsic collinearity (eg weight and age in newborns). Anyway my question relates to the use of the standardisation recommended by Nick and Diane versus centring for computational purposes. Nicks model standardises parameters as follows: > In fact I suggested standard values of 70 kg, 40 years and 6 > L/h creatinine clearance. etc. However if the standardising value is quite different from the mean (or some other descriptor of the central tendency of the distribution) then is it possible that the beneficial computational effects of centring will be lost? eg standardised creatinine clearance is 6 L/h but the sample average is 4.2 L/h... How far away can the standardised value be from the centre value to retain centring benefits? Any loss of beneficial effects of centring will of course bring all the usual problems associated with collinearity. Any thoughts? Steve ================= Stephen Duffull School of Pharmacy University of Queensland Brisbane, QLD 4072 Australia Ph +61 7 3365 8808 Fax +61 7 3365 1688 http://www.uq.edu.au/pharmacy/duffull.htm