st: New version of -smileplot- on SSC

[Roger Newson <roger.newson@kcl.ac.uk>]

Dear All

Thanks to Kit Baum, there is now a new version of the -smileplot- package 
on SSC. To describe and install the package, type -ssc describe smileplot- 
from Web-aware Stata.

The new version of the -smileplot- package represents a major overhaul. The 
package now contains two programs, -multproc- and -smileplot- (which calls 
-multproc-). -multproc- takes, as input, a variable containing multiple 
P-values and a user-specified uncorrected P-value threshold, and calculates 
a corrected P-value threshold using a choice of 11 multiple test procedure 
methods. -smileplot- calls -multproc- and then creates a smile plot, with 
data points corresponding to estimated parameters, parameter estimates on 
the X-axis, and P-values on a reverse log scale on the Y-axis. (So, the 
higher a data point is, the more statistically significant it is.) The 
Y-axis has reference lines indicating the uncorrected and corrected overall 
critical P-values. The  line indicating the corrected critical P-value is 
called the parapet line, and represents an "upper confidence limit" for the 
set of null hypotheses that are true. The methods used to calculate the 
corrected critical P-value may control the familywise error rate (eg the 
Bonferroni, Holm and Hochberg procedures) or control the false discovery 
rate (eg the Simes and Benjamini-Yekutieli procedures). If a method 
controls the familywise error rate, and the uncorrected critical P-value is 
alpha, then we are 100*(1-alpha) percent confident that all rejected null 
hypotheses are false. If a method controls the false discovery rate, then 
we are 100*(1-alpha) percent confident that, if there are any rejected null 
hypotheses, then at least some of them are false. (In the limit, as the 
number of multiple tests tends to infinity, it may be argued that, if the 
FDR is controlled at alpha, then we are 100% confident that 100*(1-alpha) 
percent of rejected null hypotheses are false.) False discovery rate (FDR) 
is a fashionable idea in statistics at the moment (rightly or wrongly), and 
new FDR-controlling procedures come out all the time. I have done some 
preliminary certification of the package, testing its results against 
results in the literature.

Best wishes

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: roger.newson@kcl.ac.uk

Opinions expressed are those of the author, not the institution.

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