At 00:43 15/04/04 +0900, Joseph Coveney wrote (in reply to Gregory Daniel):
I think that this can be done in Stata using -simulate-. Samples of various
sizes with given areas under the reciever operating characteristic curves
could
be created with -drawnorm outcome predictor, corr(1 `rho' \ `rho' 1)
n(`sample_size')-. Then -replace outcome = outcome > 0.5-, and -replace- the
predictor according to the assumption for its distribution. (For a normally
distributed predictor, AUC given by -lroc- after -probit outcome
predictor- is
(`rho' + 1) / 2). Create two such samples (one for each of the two
indepedent-
sample comparison groups) with their given AUCs determined by their
respective
`rho' values used in -drawnorm-, and -append- them, using a grouping
indicator
variable to tell them apart and to reference in the -by- option in
-roccomp-.
Power to discriminated the two groups at each sample size and level of Type I
error rate can then be estimated based upon the results returned from
-roccomp-, with the number of replications determined by the precision
desired
for the power estimate.
Another useful tool might be the -powercal- package, downloadable from SSC.
If you can estimate a standard error for a known sample size using
-simulate-, then you can use that standard error for input to power
calculations using -powercal-, as described in the -powercal- manual
(distributed with the -powercal- package).
Roger
--
Roger Newson
Lecturer in Medical Statistics
Department of Public Health Sciences
King's College London
5th Floor, Capital House
42 Weston Street
London SE1 3QD
United Kingdom
Tel: 020 7848 6648 International +44 20 7848 6648
Fax: 020 7848 6620 International +44 20 7848 6620
or 020 7848 6605 International +44 20 7848 6605
Email: [email protected]
Website: http://www.kcl-phs.org.uk/rogernewson
Opinions expressed are those of the author, not the institution.
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