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st: Re: xsampsi produces inconsistent results


From   "Joseph Coveney" <jcoveney@bigplanet.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Re: xsampsi produces inconsistent results
Date   Sun, 6 Jun 2010 13:00:10 +0900

Michael McCulloch wrote:

I'm using -xsampsi- to estimate sample sizes for a crossover experiment.
I compared two examples, each with alpha=0.25, and beta=0.20.

Experiment 1 (where mean difference =30, and its standard deviation =  
45):
	xsampsi, alpha(0.025) beta(0.20) delta(30) stddev(45)

Experiment 2  (where mean difference =2.3, and its standard deviation  
= 0.63):
	xsampsi, alpha(0.025) beta(0.20) delta(2.3) stddev(.63)

The first example produces a result, but the second leads to error  
message:
	"Need to install package 'nct' to compute non-central t distribution"

However, I have installed -nct-.

Any feedback would be appreciated!

--------------------------------------------------------------------------------

The complete output of your second case is shown below.

You can see that it's trying to compute a t statistic of an N of 1, which gives 
rise to a negative degrees of freedom for the quantile computation, which gives 
rise to a missing value for the t quantile, which when fed to -nct- (set trace 
on to see the call to -nct-) gives rise to a nonzero return code, which is 
trapped with the omnibus (and in this case erroneous) error message.

-xsampsi- is user-written, not official.  I recommend contacting the author 
with the bug report.

In the meantime, you can use simulation to do your power analysis.

Joseph Coveney

. xsampsi, alpha(0.025) beta(0.20) delta(2.3) stddev(.63)
Sample size for cross-over trial (AB/BA)
according to Senn 1991:  'Cross-over trials in clinical research'
Parameters below expressed in terms of the basic estimator, the treatment
contrast.
---------------

Alpha level    = 0.025  Power      = 0.800
Zalpha         = 2.241  Zbeta      = 0.842

Detectable effect size             = 2.300
Std.dev. of basic estimator        = 0.630

Approximate N (normal)             =    1
Critical value of t(df=-1,0.013)   =    .
Noncentrality parameter of t       = 3.651
Need to install package 'nct' to compute non-central t distribution
r(999);


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