LISTSERV at the University of Georgia
Menubar Imagemap
Home Browse Manage Request Manuals Register
Previous messageNext messagePrevious in topicNext in topicPrevious by same authorNext by same authorPrevious page (June 2010, week 2)Back to main SAS-L pageJoin or leave SAS-L (or change settings)ReplyPost a new messageSearchProportional fontNon-proportional font
Date:         Mon, 14 Jun 2010 10:57:16 -0400
Reply-To:     Ryan Black <>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Ryan Black <>
Subject:      Re: when to do separate analysis for gender
In-Reply-To:  <>
Content-Type: text/plain; charset=ISO-8859-1


Could you provide a reference for retaining interaction terms that have p-values =<.25?



On Mon, Jun 14, 2010 at 10:49 AM, Viel, Kevin <> wrote:

> > -----Original Message----- > > From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of > Myung > > Ki > > Sent: Monday, June 14, 2010 8:42 AM > > To: SAS-L@LISTSERV.UGA.EDU > > Subject: when to do separate analysis for gender > > > > Hello everybody, > > > > I have a question on when we need to do separate analysis and when to do > > combined analysis. > > To decide this, we have done descriptive analysis by gender and test many > > interaction by gender term as well. > > But the results were not straightforward for that decision. > > > > I prefer to do separate analysis complemented by gender interaction. This > > is > > because although combined analysis is able to present estimates for sex > > variable, which separate analysis cannot, it doesn't allow comparison > > between gender with values. But others in my group think differently. > > > > So I would like to your opinion and possibly a reference on this issue, > > Any comments will be appreciated. > > Myung, > > We need much more information. For instance, your model and a brief > description of study design. The "interaction" might not sufficiently be > handled by inclusion of the term "sex*exposure". How you code the terms > could be a problem. > > I think separate analyses are justifiable as the more conservative > approach. Remember, that you can model a circle with a triangle, but the > fit might not be the best. Sex as a dichotomous variable, to me, is always > questionable, but how objectionably so depends on the study question. > > Note that I used the term sex and you used gender. I made an assumption. > I prefer gender as a behavioural construct and sex as a physical one. > Despite being classically treatment as a dichotomous trait, sex is not male > or female on a refined level. For instance, X0 (Turner's syndrome) and XXY > (Klinefelter's syndrome) occur more frequently than some might expect. > Also, the levels of "sex" hormones do not always accurately classify > physical or karyotypic sex. However, as measurements of sex hormones or > other traits are rarely done, we are usually stuck with female and male sex. > > By the way, I have seen recommendations that p-values, if you will, on > interaction terms as high as 0.25 should mean that the term should be > retained in the absence of a (strong) a priori argument. > > -Kevin > > > > Kevin Viel, PhD > Senior Research Statistician > Patient Safety & Quality > International College of Robotic Surgery > Saint Joseph's Translational Research Institute > > Saint Joseph's Hospital > 5671 Peachtree Dunwoody Road, NE, Suite 330 > Atlanta, GA 30342 > > (678) 843-6076: Direct Phone > (678) 843-6153: Facsimile > (404) 558-1364: Mobile > > Confidentiality Notice: > This e-mail, including any attachments is the > property of Catholic Health East and is intended > for the sole use of the intended recipient(s). > It may contain information that is privileged and > confidential. Any unauthorized review, use, > disclosure, or distribution is prohibited. If you are > not the intended recipient, please delete this message, and > reply to the sender regarding the error in a separate email. >

Back to: Top of message | Previous page | Main SAS-L page