[R] using a noisy variable in regression (not an R question)

John Sorkin jsorkin at grecc.umaryland.edu
Sat Mar 7 19:14:07 CET 2009

The answer is simple - add the measured value as an independent variable to the regression. There is no need to convert continuous values to categorical values. If there is a circadian rhythm to the hormone secretion (e.g. cortisol) I would try to get values at the same time of day for all study participants. Baring this, perhaps you could adjust both for the hormone concentration and the time of day the sample was obtained.  

John David Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)

>>> Juliet Hannah <juliet.hannah at gmail.com> 3/7/2009 12:49 PM >>>
Hi, This is not an R question, but I've seen opinions given on non R
topics, so I wanted
to give it a try. :)

How would one treat a variable that was measured once, but is known to
fluctuate a lot?
For example, I want to include a hormone in my regression as an
explanatory variable. However, this
hormone varies in its levels throughout a day. Nevertheless, its levels differ
substantially between individuals so that there is information there to use.

One simple thing to try would be to form categories, but I assume
there are better ways to handle this. Has anyone worked with such data, or could
anyone suggest some keywords that may be helpful in searching for this
topic. Thanks
for your input.



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