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RE: st: Spearman correlation with adjustment

From   "Tiago V. Pereira" <>
Subject   RE: st: Spearman correlation with adjustment
Date   Fri, 7 Oct 2011 16:09:16 -0300 (BRT)


You might explore the following approach (and see if it makes some sense
in your case):

Assumption:  there are no ties. So, you can compute  spearman's
coefficient (rho_S) from  pearson's coefficient (rho_P)


1) Create two or more categories or subgroups in which the confounding
variable has a smaller role
2) Within each category compute ranks for your values
2) Calculate the pearson coefficient using those ranks (that is, rho_P
will be calculated from ranked variables)
3) transform the rho_Ps into Z scores (r to z' transformation - Fisher
4) perform a meta-analysis of Z scores
5) get the results back to the original metric (rho_P)

This approach is likely to provide less biased results compared to raw
analyses. It also provides the opportunity to check/quantify if there is
statistical heterogeneity among subgroups (Cochran's Q test, I^2 index).

All you need is the rho_P from ranked variables and the following packages
-corrci- (or -corrcii-) and -metan-

also check:



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