On 5 March 2010 03:26, BXC (Bendix Carstensen) <b...@steno.dk> wrote:
> You can actually also get results from fisher.test when one of the entries is
> 0.
> One of your confidence limits will just be either 0 or Inf.
> But that should hardly be a surprise.
>
> You could also try the twoby2 command from the Epi package which will
> summarize your
> table analysis nicely.
>
>> m
> [,1] [,2]
> [1,] 360 0
> [2,] 7 120
>> fisher.test(m)
>
> Fisher's Exact Test for Count Data
>
> data: m
> p-value < 2.2e-16
> alternative hypothesis: true odds ratio is not equal to 1
> 95 percent confidence interval:
> 1204.706 Inf
> sample estimates:
> odds ratio
> Inf
Another approach is to run a logistic regression model. Using the same
data as above:
> (dp <- data.frame (a=c(320,7,0,0), b=c(0,0,4,120), t=c(1,0,1,0)))
a b t
1 320 0 1
2 7 0 0
3 0 4 1
4 0 120 0
> fit <- glm(cbind(a,b) ~ t, binomial, dp)
The estimated odds ratio is here (OR=1371):
> exp(coef(fit))
(Intercept) t
5.833e-02 1.371e+03
And its confidence interval:
> exp(confint(fit))
Waiting for profiling to be done...
2.5 % 97.5 %
(Intercept) 0.02466 0.1159
t 442.87000 5551.2516
With less unbalanced data, glm() gives results very close to those
from fisher.test()
> dp2
a b t
1 320 0 1
2 200 0 0
3 0 100 1
4 0 150 0
> fit2 <- glm(cbind(a,b) ~ t, binomial, dp2)
> exp(coef(fit2))
(Intercept) t
1.333 2.400
> exp(confint(fit2))
Waiting for profiling to be done...
2.5 % 97.5 %
(Intercept) 1.080 1.650
t 1.766 3.274
> fisher.test(matrix(c(320,200,100,150),2))
Fisher's Exact Test for Count Data
data: matrix(c(320, 200, 100, 150), 2)
p-value = 2.294e-08
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.742 3.309
sample estimates:
odds ratio
2.397
--Philippe
Senior Epidemiologist,
World Health Organization
Geneva, Switzerland
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