# [R] replicating the odds ratio from a published study

Bob Green bgreen at dyson.brisnet.org.au
Fri Jan 26 22:26:45 CET 2007

```Peetr & Michael,

I now see my description may have confused the issue.  I do want to compare
odds ratios across studies - in the sense that I want to create a table
with the respective odds ratio for each study. I do not need to
statistically test two sets of odds ratios.

What I want to do is ensure the method I use to compute an odds ratio is
accurate and intended to check my method against published sources.

The paper I selected by Schanda et al (2004). Homicide and major mental
disorders. Acta Psychiatr Scand, 11:98-107 reports a total sample of 1087.
Odds ratios are reported separately for men and women. There were 961 men
all of whom were convicted of homicide. Of these 961 men, 41 were diagnosed
with schizophrenia. The unadjusted odds ratio is for this  group of 41 is
cited as 6.52   (4.70-9.00).  They also report the general population aged
over 15 with schizophrenia =20,109 and the total population =2,957,239.

Any further clarification is much appreciated,

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 at biostat.ku.dk)                  FAX: (+45) 35327907

```