[R] Regression versus functional/structural relationship?
Leif Peterson
peterson.leif at ieee.org
Thu Oct 30 02:18:32 CET 2008
The two test outcomes will have correlated results, so you will need to look
at either bivariate probit regression or seemingly unrelated regression.
For either of these two methods, you will need to constrain all independent
variable coefficients to be equal, or you will have difficulty making sense
of the results. Stata has biprobit and sureg, and also a constraint
command. (Also bivariate probit requires binary dependents, so you will
need to apply a "clinically interesting" cutpoint of (+)/(-) test results.
If you can't find anything like these in R you will likely need to perform
quantile normalization of both dependents (x,y) before regression. Look at
the qnorm package in bioconductor, by Bolstad. LP
-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Ravi Varadhan
Sent: Wednesday, October 29, 2008 6:01 PM
To: r-help at stat.math.ethz.ch
Subject: [R] Regression versus functional/structural relationship?
Hi,
I am dealing with the following problem. There are two biochemical assays,
say A and B, available for analyzing blood samples. Half the samples have
been analyzed with A. Now, for some insurmountable logistic reasons, we
have to use B to analyze the remaining samples. However, we can do a
comparative study on a small number of samples where we can obtain
concentrations using both A and B. This gives us the data of the form (x,
y), where x are values from A and y from B. Now, my question: Can we
simply use the regression equation from regressing y on x, to convert all
the x values for which only method A was used? Or do we need to obtain the
functional (or structural) relationship between X and Y (the true values
without measurement error) and use that to do this conversion. It seems to
me that since we can only observe error-prone x, and we should be predicting
the expected value of error-prone y (i.e E[y | x]). Therefore, we can
simply use the ordinary regression equation. However, I have seen papers
using the Deming's orthogonal regression or something equivalent in the
clinical chemistry literature to address this problem. Deming's method
would make sense if I am interested in obtaining the functional relationship
between X and Y (the true values of two assays), but I don't see why I
should care about that. Am I right?
I would appreciate any clarifying thoughts on this. I apologize for posting
this methodological, non-R question.
Thank you,
Ravi.
----------------------------------------------------------------------------
-------
Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: rvaradhan at jhmi.edu
Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html
----------------------------------------------------------------------------
--------
[[alternative HTML version deleted]]
______________________________________________
R-help at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
More information about the R-help
mailing list