[R-sig-ME] Data snooping in repeated measures study?

Afshartous, David d.afshartous at Vanderbilt.Edu
Sun Jan 9 01:39:53 CET 2011

Paul, although your scenario sounds a bit different you might find my paper with Wolf useful:

Avoiding ‘data snooping’ in multilevel and mixed effects models
JRSS-A, Volume 170, Issue 4, pages 1035–1059, October 2007


From: r-sig-mixed-models-bounces at r-project.org [r-sig-mixed-models-bounces at r-project.org] On Behalf Of Prew, Paul [Paul.Prew at ecolab.com]
Sent: Friday, January 07, 2011 3:27 PM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Data snooping in repeated measures study?

Hello, I've been asked to perform an analysis that I'm not sure how to frame properly, or even if it can be performed validly.

There are 21 independent batches ("Lots") of a chemical that were measured repeatedly over a number of months.  The response measured was the concentration of the active ingredient "CHG", with interest in how the CHG decays over time.  One Lot had a slope much lower than the other Lots.  Is it possible to test the slope of this Lot for statistical significance, with the null hypothesis that the slope is no different than the overall slope for this chemical?  Or would any test constructed just be data snooping, invalidating any inference?  Can anyone suggest a valid approach using lme?

> str(Conc.dat)
'data.frame':   121 obs. of  3 variables:
 $ Lot : Factor w/ 21 levels "L012391","L012471",..: 16 16 16 16 16 16 16 16 10 10 ...
 $ Days: int  30 121 217 307 399 583 765 766 78 176 ...
 $ CHG.: num  2.06 2.01 1.97 1.94 1.88 …

All of the Lots were measured over 2 years time, but have different numbers of intermediate measurements.

Thank you, Paul

Paul Prew   ▪  Statistician
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