Thank you all R friends,

I did think bout running a univariate logistic regression could work but in
my case I guess the fisher.test() will work fine.

Best regards to all,

Abraço forte e que a força esteja com você,

Dr. Pedro Emmanuel A. A. do Brasil
Instituto de Pesquisa Clínica Evandro Chagas
Fundação Oswaldo Cruz
Rio de Janeiro - Brasil


2010/3/6 <r-sig-epi-request@stat.math.ethz.ch>

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>   1. Re: epitools::oddsratio error numbers close to zero in    cells?
>      (Philippe Glaziou)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Fri, 5 Mar 2010 13:53:48 +0100
> From: Philippe Glaziou <glaziou@gmail.com>
> To: "r-sig-epi@stat.math.ethz.ch" <r-sig-epi@stat.math.ethz.ch>
> Subject: Re: [R-sig-Epi] epitools::oddsratio error numbers close to
>        zero in cells?
> Message-ID:
>        <3d3804221003050453s4931c07bk42ffe4bfc5b8b4a7@mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> On 5 March 2010 03:26, BXC (Bendix Carstensen) <bxc@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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