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RE: SV: RE: Re: st: Linear Trend Tests of ORs


From   Rho YH <mania1@korea.ac.kr>
To   statalist@hsphsun2.harvard.edu
Subject   RE: SV: RE: Re: st: Linear Trend Tests of ORs
Date   Tue, 23 May 2006 23:53:23 +0900 (KST)

Yes, I have heard about this type of test, but didn't mention it because I couldn't readily
understand it.
Can I know the source of this test (which means, references) and any actual papers that used this
test?
Still, any other recommendations meanwhile are welcome.
> ---- Original Message ----
> From : "Kim Lyngby Mikkelsen (KLM)" [klm@ami.dk]
> To : statalist@hsphsun2.harvard.edu
> Date : 2006년 5월 23일(화) 19:32:21
> Subject : SV: RE: Re: st: Linear Trend Tests of ORs
>
>
>Young Hee Rho wrote:
>I have encountered many "trend tests" of linearity concerning odds ratios (OR) of a categorical
variable.
>For example, I am modeling a logistic model Y=b1x1 + b2x2 + b3x3 +b4. x2 is a 5-level categorical
variable, for example the level of drinking (while Y is the presence/absence of hyperuricemia). When
the results are displayed, the ORs of the 5 levels are shown and the linear trend is shown as a single
p value. The individual ORs may not have significance, however the overall trend does.
>
>_______________________________________________________________________
>
>
>To do a test for linear trend, you may use the log likelihood ration test!
>
>First you run the logistic regression with the categorical variable expanded by 'xi' (getting 4
estimates relative to the reference category of b2) and store the log likelihood in 'A':
>Model 1
>xi:logistic y b1 i.b2 b3 b4
>estimate store A
>
>then you repeat the regression without the 'xi' expansion of the categorical variable (Now you only
one estimate of b2, which is the linear effect of b2).
>Model 2
>xi:logistic y b1 b2 b3 b4
>(Note: the 'i.' in front of b2 is removed).
>
>You then simply need to se if the reduced model (model 2) is as good as your previous model
(Model 1). You do that using the likelihood ration test:
>
>lrtest A
>
>To conclude that you have a linear trend the p-value of the lrtest needs to be insignificant (Model 2
is not significantly worse than Model 1) AND the estimate for b2 (the linear effect per category in
model 2) must be significant!
>
>
>
>
>
>Kim Lyngby Mikkelsen
>Stilling?
>Seniorforsker?
>Uddannelse?
>Cand.med. Ph.D.?
>Telefon?
>39165467?
>Email?
>klm@ami.dk?
>
>
>
>
>
>-----Oprindelig meddelelse-----
>Fra: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] P?
vegne af Rho YH
>Sendt: 23. maj 2006 05:23
>Til: statalist@hsphsun2.harvard.edu
>Emne: RE: RE: Re: st: Linear Trend Tests of ORs
>
>I have just found out that the tabodds command may meet what I wanted - linear trend of ORs,
>however making multivariate adjustments is not easy (I tried and it gave no results after adjusting
>for >2 or 3 variables.) Is there any "immediate command" by just inputing the OR (and CI, if needed)
>and the independent variable category and produces a p value?
>> ---- Original Message ----
>> From : Rho YH [mania1@korea.ac.kr]
>> To : statalist@hsphsun2.harvard.edu
>> Date : 2006년 5월 23일(화) 09:43:23
>> Subject : RE: Re: st: Linear Trend Tests of ORs
>>
>>
>>Hmm.. It looks like the aformentioned Cochrane-Armitage Test, however I'll check it out.
>>Thanks.
>>> ---- Original Message ----
>>> From : Suzy [scott_788@wowway.com]
>>> To : statalist@hsphsun2.harvard.edu
>>> Date : 2006년 5월 22일(월) 21:24:22
>>> Subject : Re: st: Linear Trend Tests of ORs
>>>
>>>
>>>Perhaps Szklo and Nieto's book can help: Epidemiology. Beyond the
>>>Basics, discusses test for trend (dose reponse) in Appendix B (pp 459-462).
>>>
>>>Formula is from Mantel:
>>>
>>>Mantel N. Chi square tests with one degree of freedom: etensions of the
>>>Manetel-Haenszel procedure. J Am Stat Assoc. 1963;58: 690-700.
>>>
>>>Hope this helps.
>>>Suzy
>>>
>>>Young Hee Rho wrote:
>>>
>>>>I have encountered many "trend tests" of linearity concerning odds ratios (OR) of a
>>>>categorical variable.
>>>>For example, I am modeling a logistic model Y=b1x1 + b2x2 + b3x3 +b4. x2 is a 5-level
>>>>categorical variable, for example the level of drinking (while Y is the presence/absence of
>>>>hyperuricemia). When the results are displayed, the ORs of the 5 levels are shown and
>>>>the linear trend is shown as a single p value. The individual ORs may not have significance,
>>>>however the overall trend does. It is said that it was tested through regressing the median of
>>>>the levels on the ORs. Otherwise in other cases, there are many trend tests of linearity
>>>>expresed in many papers, however, the actual method is not explained in detail. (It does not
>>>>apear to come from polynomial contrasts of ANOVA nor from categorical trend tests
>>>>(Cochrane-Armitage) since the arformentioned test is from values coming from
>>>>one categorical variable having several estimates. How is this done and how much methods
>>>>exsist on this topic? Are there any useful references?
>>>>** For those who got twice this article, I sent this article again since it did not seem to register
on
>>>>Statalist. Many apologies if there was a duplicate delivery.
>>>>
>>>
>>>
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>>>
>>>
>>>
>>
>>
>>
>
>*
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>
>



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