From: Bob Green <bgreen_at_dyson.brisnet.org.au>

Date: Sun 28 Jan 2007 - 21:13:46 GMT

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon Jan 29 12:52:44 2007

Date: Sun 28 Jan 2007 - 21:13:46 GMT

Michael,

Thanks. Yes, clearly the volume number for the Schanda paper I cited is wrong.

For the present purpose, my primary question is: as you have now seen the Schanda paper, would you consider Schanda calculated odds or relative risk?

Also, when I tried the formula suggested by Peter (below) I obtained an error - do you know what M might be or the source of the error?

exp(log(41*2936210/920/20068)+qnorm(c(.025,.975))*sqrt(sum(1/M))) Error in sum(1/M) : object "M" not found

> eronen1 <- as.table(matrix(c(58,852,13600-58,1947000-13600-852), ncol = 2 , dimnames = list(group=c("scz", "nonscz"), who= c("sample", "population")))) > fisher.test(eronen1)

p-value < 2.2e-16

alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:

7.309717 12.690087

sample estimates:

odds ratio

9.713458

> eronen2 <- as.table(matrix(c(86,1302,13530-86,1933000-13530-1302), ncol
= 2 , dimnames = list(group=c("scz", "nonscz"), who= c("sample",
"population"))))

> fisher.test(eronen2)

p-value < 2.2e-16

alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:

7.481272 11.734136

sample estimates:

odds ratio

9.42561

References

Eronen, M. et al. (1996 - 1) Mental disorders and homicidal behavior in Finland. Archives of General Psychiatry, 53, 497-501

Eronen, M et al (1996 - 2). Schizophrenia & homicidal behavior. Schizophrenia Bulletin, 22, 83-89

Woodward, Mental disorder & homicide. Epidemiologia E Psichiatria Sociale, 9, 171-189

Any comments are welcomed,

Bob

At 01:57 PM 28/01/2007 +0000, Michael Dewey wrote:

>At 22:01 26/01/2007, Peter Dalgaard wrote:

*>>Bob Green wrote:
**>>>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.
**>
**>Looking at the paper (which is in volume 110 by the way) suggests that
**>Peter's reading of the situation is correct and that is what the authors
**>have done.
**>
**>>>Any further clarification is much appreciated,
**>>>
**>>A fisher.test on the following matrix seems about right:
**>> > matrix(c(41,920,20109-41,2957239-20109-920),2)
**>>
**>> [,1] [,2]
**>>[1,] 41 20068
**>>[2,] 920 2936210
**>>
**>> > fisher.test(matrix(c(41,920,20109-41,2957239-20109-920),2))
**>>
**>> Fisher's Exact Test for Count Data
**>>
**>>data: matrix(c(41, 920, 20109 - 41, 2957239 - 20109 - 920), 2)
**>>p-value < 2.2e-16
**>>alternative hypothesis: true odds ratio is not equal to 1
**>>95 percent confidence interval:
**>>4.645663 8.918425
**>>sample estimates:
**>>odds ratio
**>> 6.520379
**>>
**>>The c.i. is not precisely the same as your source. This could be down to
**>>a different approximation (R's is based on the noncentral hypergeometric
**>>distribution), but the classical asymptotic formula gives
**>>
**>> > exp(log(41*2936210/920/20068)+qnorm(c(.025,.975))*sqrt(sum(1/M)))
**>>[1] 4.767384 8.918216
**>>
**>>which is closer, but still a bit narrower.
**>
**>Michael Dewey
**>http://www.aghmed.fsnet.co.uk
*

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