Multiple comparison correction?
Here (http://andrewgelman.com/2014/10/14/one-lifes-horrible-ironies-wrote-paper-usually-dont-worry-multiple-comparisons-now-spend-lots-time-worrying-multiple-comparisons/) is a more recent discussion of Gelman's view on topic. Daniel B. Wright, Ph.D. Statistical Research Division 8701 N. MoPac Expressway, Suite 200, Austin, TX 78759?? (preferred method of communication is email, use cell if urgent) Office: 512.320.1827 Cell: 786 342 4656 This email message is intended only for the personal use of the recipient(s) named above. If you are not an intended recipient, you may not review, copy, or distribute this message. If you have received this communication in error, please notify the sender immediately by email and delete the original?message. -----Original Message----- From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Malcolm Fairbrother Sent: Thursday, November 06, 2014 4:39 PM To: ahnate at gmail.com Cc: r-sig-mixed-models Subject: Re: [R-sig-ME] Multiple comparison correction? Dear Ahnate, I'm not a particular expert on this topic, but I found Gelman et al.'s views quite interesting: http://www.stat.wisc.edu/~larget/Stat998/Fall2013/GelmanMultipleComparisons.pdf
From the sounds of it you'll find the paper useful too.
Cheers, Malcolm
Date: Wed, 5 Nov 2014 12:49:33 -1000 From: Ahnate Lim <ahnate at gmail.com> To: r-sig-mixed-models at r-project.org Subject: [R-sig-ME] Multiple comparison correction? Hello, I have a question related to mixed effect modeling and how to do multiple comparisons. We have a longitudinal study with different groups and many dependent variables such as of brain cortical volume in different areas, etc. I am using lme, and remember reading somewhere that multiple comparison corrections do not actually apply to linear mixed effects models, due to the statistics involved. For example, if I run the same model on 100 dependent variables, traditionally I would need to correct for multiple comparisons by dividing the alpha level (0.05) by 100 to get the proper criterion of 0.0005, adjusting for the increased likelihood of getting type I errors. I am wondering however, if this process is the same, or even necessary at all for lme models? Thank you, Ahnate
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