|Date: ||Fri, 29 Sep 2006 08:33:10 -0400|
|Reply-To: ||Peter Flom <Flom@NDRI.ORG>|
|Sender: ||"SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>|
|From: ||Peter Flom <Flom@NDRI.ORG>|
|Subject: ||Re: interactions (effect measure modification)|
|Content-Type: ||text/plain; charset=US-ASCII|
>>> Kevin Roland Viel <kviel@EMORY.EDU> 9/28/2006 5:58 pm >>> wrote
One published report performed separate analyses for male and females.
A reviewer suggested that we also perform such an analyses. I disagree
based on current knowledge, but I performed the analyses anyway. We
have approximately 360 people in this study.
In the combined analysis using a interaction term the results were:
Score (-1,0,1): 22.1 (p = 0.17)
Score X SNP: -6.1 (p = 0.59)
Score (0,1): 6.6 (p = 0.52)
Score(-1,1): 19.0 (p < 0.01)
In each analyses, score is an additive effect, i.e. one variable, up
three levels. Males cannot (usually, see PS) have a score of 0, but
female had a score of -1.
Under such scoring we might expect:
In other words, the means of the group with score = 0 is the average
the means of the groups with score = -1 and score = 1.
Translating the male results to the female results would mean the
comparison at hand, 0/1, would have an effect size of 9.5.
the female results to the male results would mean the comparison at
hand, -1/1, would have an effect size of 13.2.
I would like to reply that there is not enough evidence to suggest
sex is an effect measure modifier. Nominally, 6.6 is different from
(or 13.2 is different from 19.0), but I am not willing to say that a
sample size (with -1 females) would support the contention that an
interaction term or separate analyses should be the main results.
Would anyone like to comment or suggest another type of analyses?
I may be missing something, but I don't understand what you are doing
here. What was the DV, and how was it scored? Clearly there must be
some IV besides sex, but what was it? Was there more than one? How was
the IV that was in the interaction scored?
What statistical methods did you use?
In general, though, the question of whether to look at interactions or
do stratified analysis should be answered based on the nature of the
research question. Stratified analyses are easier to interpret, but do
not give all the infomration that the analysis with interaction gives -
in particular, they do not allow about the size and significance of the
difference in effect size