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st: New upgrade of -smileplot- on SSC


From   Roger Newson <roger.newson@kcl.ac.uk>
To   statalist@hsphsun2.harvard.edu
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

--
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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