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st: New package -regpar- on SSC


From   "Roger B. Newson" <r.newson@imperial.ac.uk>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: New package -regpar- on SSC
Date   Tue, 01 Nov 2011 15:08:27 +0000

Thanks once again to Kit Baum, a new package -regpar- is now available for download from SSC. In Stata, use the -ssc- command to do this.

The -regpar- package is described as below on my website, and estimates population unattributable and attributable risks (PURs and PARs) from binary regression models. Note that PURs and PARs are not the same as population unattributable and attributable fractions (PUFs and PAFs), because PUFs and PAFs are ratios of arithmetic means (which include risks), whereas PURs and PARs are risks and risk differences, respectively.

Best wishes

Roger


--
Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: r.newson@imperial.ac.uk
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/

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

---------------------------------------------------------------------------
package regpar from http://www.imperial.ac.uk/nhli/r.newson/stata12
---------------------------------------------------------------------------

TITLE
      regpar: Population attributable risks from binary regression models

DESCRIPTION/AUTHOR(S)
      regpar calculates confidence intervals for population attributable
      risks, and also for scenario proportions.  regpar can be used after
      an estimation command whose predicted values are interpreted as
      conditional proportions, such as logit, logistic, probit, or glm.
      It estimates two scenario proportions, a baseline scenario
      ("Scenario 0") and a fantasy scenario ("Scenario 1"), in which one
      or more exposure variables are assumed to be set to particular
      values (typically zero), and any other predictor variables in the
      model are assumed to remain the same.  It also estimates the
      difference between the Scenario 0 proportion and the Scenario 1
      proportion.  This difference is known as the population
      attributable risk (PAR), and represents the amount of risk
      attributable to living in Scenario 0 instead of Scenario 1.

      Author: Roger Newson
      Distribution-Date: 31october2011
      Stata-Version: 12

INSTALLATION FILES                                  (click here to install)
      regpar.ado
      regpar_p.ado
      regpar.sthlp
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