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st: New upgrade of -smileplot- on SSC

From   Roger Newson <>
Subject   st: New upgrade of -smileplot- on SSC
Date   Wed, 06 Nov 2002 12:40:36 +0000

Dear All

Thanks to Kit Baum, there is now a new upgrade of my -smileplot- package downloadable on SSC. Type -net desc smileplot-, -ssc desc smileplot- or -findit smileplot- to find out more.

The -smileplot- package is a data mining tool for use with multiple parameter estimates. It takes, as input, a data set with 1 obs per estimated parameter for one or more models, and data on the P-values, estimates, and other parameter attributes. (Such a data set might be created by the packages -parmest- and/or -dsconcat-.) The -smileplot- package contains 2 programs, -multproc- and -smileplot-. -multproc- carries out multiple test procedures on the set of P-values, with a choice of methods (eg the Bonferroni method or the much less conservative Simes method). -smileplot- calls -multproc- and then creates a smile plot, plotting the P-values on a reverse log scale on the Y-axis against the estimates on the X-axis, with reference lines on the Y-axis corresponding to the uncorrected and corrected P-value thresholds. The smile plot therefore enables a user to see simultaneously both the statistical significance and the practical significance of each parameter estimate. (Note, however, that the X-axis does not have to be the parameter estimate. For instance, in a genome scan, it might be position of a gene on a chromosome.)

In the new version, both -multproc- and -smileplot- are byable. Therefore, with a single command line, the user can carry out the same multiple test procedure on more than one by-group of parameter estimates. These by-groups might be a set of unadjusted odds ratios and the corresponding set of confounder-adjusted odds ratios. There are also added optional generated output variables, containing the uncorrected overall P-value threshold for the by-group, the corrected overall P-value threshold for the by-group, and an indicator variable equal to one if the null hypothesis corresponding to a parameter is rejected and equal to zero if this null hypothesis is not rejected. Therefore, a user can use the same multiple test procedure on a large number of unadjusted odds ratios and the corresponding confounder-adjusted odds ratios, and list the ones that appear "significant" before and after adjusting for the confounders.

Best wishes


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

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