Re: Correlations for power analysis

From: "Paul Williams" <pwilliams@uop.edu> Subject: Re: Correlations for power analysis Date: Fri, 08 Dec 2000 08:41:38 -0800 Paul, I am assuming from your note that you are going to plot prediction [x axis] vs observed [y axis]. If this is your approach you should be interested in more than the correlation because there can still be some systematic error in the model even when there is a high degree or correlation between the predicted and observed. If this is your approach then you should also be interested in the slope [slope should not be different from 1] and intercept [intercept should not be different from 0] of this regression in addition to the correlation between the predicted and observed. Of course the whole problem with regression is the strong influence of outliers. There are ways to deal with these ourliers also. If it appears that outliers are a problem you could take a look at a text "Introduction to Robust Estimation and Hypothesis Testing" by Rand R. Wilcox. What is a good correlation would depend on the intended use of the model and what are the consequences of having a poor correlation. If you decide to go ahead with the predictive performance parameter method, see a paper in Pharmacotherapy entitled "Direct Comparison of Three Methods for Predicting Digoxin Concentrations, November 1996. In this paper we outlined and applied a validation method that accounts for several nonindependent and unbalanced observations within the same subject. Cheers! Paul Williams