# Re: [R] replicating the odds ratio from a published study- post # 2

From: Bob Green <bgreen_at_dyson.brisnet.org.au>
Date: Fri 26 Jan 2007 - 21:46:28 GMT

Peter & Michael,

I just came across the following on another mailing list and realized that my use (and the authors of the article use of the term 'odds ratio' ) is probably incorrect. I believe my interest is in the 'odds' of schizophrenia among the population of homicide, rather than a comparison of odds .

"Yours seems a good idea, Kevin, if you are only interested in computing the ODDS of disease (not the odds RATIO). The odds of disease equal the probability of disease divided by the probability of non-disease, i.e. p/(1-p), where p is the proportion of cases at or above the cutoff point. An odds RATIO is the ratio of two odds, e.g. the odds for vaccinated"

If it is the odds advice regarding the appropriate R script would be useful.

regards

Bob Green

., At 02:57 PM 26/01/2007 +0100, Peter Dalgaard wrote:
>Michael Dewey wrote:
> > At 09:04 26/01/2007, Bob Green wrote:
> >
> >> I wanted to compare odds ratio across studies and tried to replicate
> >> the results from a study but have not been able to determine how to
> >> do this in R.
> >>
> >> The study reported a sample of 961 men, of whom 41 had a disorder.
> >> The reported raw odds ratio was 6.52 (4.70-9.00)
> >>
> >
> > For an odds ratio you require two odds from which you form the odds ratio.
> > You only have one odds.
> > Do you have another one lying around somewhere?
> >
>Alternatively, the odds ratio presumably compares two groups. If you
>know the group sizes, the two odds ratios may be reconstructed. If I
>make a wild guess that the groups are roughly equal, I might get
>
> > M <- cbind(c(460,460),c(6,35))
> > M
> [,1] [,2]
>[1,] 460 6
>[2,] 460 35
> > fisher.test(M)
>
> Fisher's Exact Test for Count Data
>
>data: M
>p-value = 7.406e-06
>alternative hypothesis: true odds ratio is not equal to 1
>95 percent confidence interval:
> 2.393209 17.104976
>sample estimates:
>odds ratio
> 5.824317
>
>Judging by the c.i., the groups are probably *not* of similar size. I
>suppose that the high-incidence group is a bit smaller so that the count
>of advverse events is more similar. M <- cbind(c(803,117),c(21,20)) is a
>bit more like it, but your (Bob's) confidence interval is narrower even
>than this.
>
>--
> O__ ---- Peter Dalgaard Ã˜ster Farimagsgade 5, Entr.B
> c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
> (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
>~~~~~~~~~~ - (p.dalgaard@biostat.ku.dk) FAX: (+45) 35327907

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