# RE: st: trend in ORs across ordered levels of a 3rd variable

 From "Dr. med. Berthold Hoppe" To statalist@hsphsun2.harvard.edu Subject RE: st: trend in ORs across ordered levels of a 3rd variable Date Tue, 22 Apr 2008 12:37:28 +0200 (CEST)

```The problem seems to be similiar to one I am actually faced with.

Have you tried an analyses like this:

logit death consc if sex==0
est store A
logit death consc if sex==1
est store B
suest A B
test [A]consc=[B]consc

Berthold

> This sounds like a task for logistic regression using the confounder and
> the risk factor.  If you want to see if there's effect modification, use
> the product of the risk factor and confounder.  You may want to
> categorize these variables.
>
> Tony
>
> Peter A. Lachenbruch
> Department of Public Health
> Oregon State University
> Corvallis, OR 97330
> Phone: 541-737-3832
> FAX: 541-737-4001
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Visintainer,
> Paul
> Sent: Monday, April 21, 2008 12:01 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: RE: st: trend in ORs across ordered levels of a 3rd variable
>
> Joseph,
>
> Thanks for your input.  But I don't think -epitab- addresses this
> question.  The output you provided gives the trend in ORs "adjusting"
> for the confounder.  What I wanted to know is whether we can detect a
> linear pattern of the ORs over levels of the confounder (which, to me,
> looks like a specific type of interaction)
>
> Another example:  suppose I want to know whether there is a difference
> in the risk (odds) of death between males and females from trauma.
> Suppose my third variable is level of consciousness (ordinal variable
> measured at 4 levels).  Say, my output shows that as level of
> consciousness decreases, the OR for gender and death increases:  (e.g.,
> ORs at each level of consciousness: 1.0 at level 1, 1.5 at level 2, 1.9
> at level 3, and 2.3 at level four), which suggests that men do worse at
> lower levels of consciousness.
>
> I suppose that one way to address this is to approach it as if
> consciousness were a continuous variable, then look at the slopes for
> consciousness in logit models run separately for men and women.
>
> I can't think of any other approach.
>
> -p
>
> ______________________________________
> Paul F. Visintainer, PhD
> School of Public Health
> New York Medical College
>
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Joseph
> Coveney
> Sent: Saturday, April 19, 2008 3:21 AM
> To: Statalist
> Subject: Re: st: trend in ORs across ordered levels of a 3rd variable
>
> Paul Visintainer wrote:
>
> Is there an approach to analyzing the trend in odds ratios across the
> ordered levels of a 3rd variable?  For example,
>
> Suppose I have the risk of obesity in high school students by gender
>
> 10  1.5
> 11  1.9
> 12  2.2
>
> There is a test of homogeneity to determine whether these ORs differ
> across grade strata.  Is there a test to determine whether the pattern
> is linear across strata?
>
> ------------------------------------------------------------------------
> --------
>
> Are you looking for something other than -tabodds-?
>
> Joseph Coveney
>
> . webuse bdesop
>
> . tabodds case alcohol [fweight = freq], or
>
> ------------------------------------------------------------------------
> ---
>      alcohol |  Odds Ratio       chi2       P>chi2     [95% Conf.
> Interval]
> -------------+----------------------------------------------------------
> ---
>         0-39 |    1.000000          .           .              .
> .
>        40-79 |    3.565271      32.70       0.0000      2.237981
> 5.679744
>       80-119 |    7.802616      75.03       0.0000      4.497054
> 13.537932
>         120+ |   27.225705     160.41       0.0000     12.507808
> 59.262107
> ------------------------------------------------------------------------
> ---
> Test of homogeneity (equal odds): chi2(3)  =   158.79
>                                   Pr>chi2  =   0.0000
>
> Score test for trend of odds:     chi2(1)  =   152.97
>                                   Pr>chi2  =   0.0000
>
>
>
>
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