Hi,
I don't have access to the article, but must presume that they are doing
something "radically different" if you are "only" getting a total sample size
of 20,000. Or is that 20,000 per arm?
Using the G*Power app that Mitchell references below (which I have used
previously, since they have a Mac app):
Exact - Proportions: Inequality, two independent groups (Fisher's exact test)
Options: Exact distribution
Analysis: A priori: Compute required sample size
Input: Tail(s) = Two
Proportion p1 = 0.00154
Proportion p2 = 0.00234
α err prob = 0.05
Power (1-β err prob) = 0.8
Allocation ratio N2/N1 = 1
Output: Sample size group 1 = 49851
Sample size group 2 = 49851
Total sample size = 99702
Actual power = 0.8168040
Actual α = 0.0462658
Using the base R power.prop.test() function:
> power.prop.test(p1 = 0.00154, p2 = 0.00234, power = 0.8)
Two-sample comparison of proportions power calculation
n = 47490.34
p1 = 0.00154
p2 = 0.00234
sig.level = 0.05
power = 0.8
alternative = two.sided
NOTE: n is number in *each* group
Using Frank's bsamsize() function in Hmisc:
> bsamsize(p1 = 0.00154, p2 = 0.00234, fraction = .5, alpha = .05, power = .8)
n1 n2
47490.34 47490.34
Finally, throwing together a quick Monte Carlo simulation using the FET, I get:
TwoSampleFET <- function(n, p1, p2, power = 0.85,
R = 5000, correct = FALSE)
{
MCSim <- function(n, p1, p2)
{
Control <- rbinom(n, 1, p1)
Treat <- rbinom(n, 1, p2)
fisher.test(cbind(table(Control), table(Treat)))$p.value
}
# Run MC Replicates
MC.res <- replicate(R, MCSim(n, p1, p2))
# Get p value at power quantile
quantile(MC.res, power)
}
# 50,000 per arm
> TwoSampleFET(50000, p1 = 0.00154, p2 = 0.00234, power = 0.8, R = 500)
80%
0.04628263
So all four of these are coming back with numbers in the 48,000 to 50,000
***per arm***.
HTH,
Marc Schwartz
On Nov 8, 2010, at 10:16 AM, Mitchell Maltenfort wrote:
> Not with R, but look for G*Power3, a free tool for power calc,
> includes FIsher's test.
>
> http://www.psycho.uni-duesseldorf.de/abteilungen/aap/gpower3
>
> On Mon, Nov 8, 2010 at 10:52 AM, Giulio Di Giovanni
> <perimessagg...@hotmail.com> wrote:
>>
>>
>> Hi,
>> I'm try to compute the minimum sample size needed to have at least an 80% of
>> power, with alpha=0.05. The problem is that empirical proportions are really
>> small: 0.00154 in one case and 0.00234. These are the estimated failure
>> proportion of two medical treatments.
>> Thomas and Conlon (1992) suggested Fisher's exact test and proposed a
>> computational method, which according to their table gives a sample size of
>> roughly 20000. Unfortunately I cannot find any software applying their
>> method.
>> -Does anyone know how to estimate sample size on Fisher's exact test by
>> using R?
>> -Even better, does anybody know other, maybe optimal, methods for such a
>> situation (small p1 and p2) and the corresponding R software?
>>
>> Thanks in advance,
>> Giulio
______________________________________________
R-help@r-project.org 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.
