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From |
"Roger B. Newson" <r.newson@imperial.ac.uk> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
st: New versions of -punaf-, -punafcc-, -regpar- and -margprev- on SSC |

Date |
Thu, 17 Oct 2013 12:43:01 +0100 |

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. TITLE punaf: Population attributable fractions for cohort studies DESCRIPTION/AUTHOR(S) punaf calculates confidence intervals for population attributable fractions, and also for scenario means and their ratio, known as the population unattributable fraction. punaf can be used after an estimation command whose predicted values are interpreted as conditional arithmetic means, such as logit, logistic, poisson, or glm. It estimates the logs of two scenario means, the 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 log of the ratio of the Scenario 1 mean to the Scenario 0 mean. This ratio is known as the population unattributable fraction, and is subtracted from 1 to derive the population attributable fraction, defined as the proportion of the mean of the outcome variable attributable to living in Scenario 0 instead of Scenario 1. Author: Roger Newson Distribution-Date: 16october2013 Stata-Version: 13 INSTALLATION FILES (click here to install) punaf.ado punaf_p.ado punaf.sthlp ------------------------------------------------------------------------------- (click here to return to the previous screen) -------------------------------------------------------------------------------------- TITLE

DESCRIPTION/AUTHOR(S) punafcc calculates confidence intervals for population attributable and unattributable fractions in case-control or survival studies. punafcc can be used after an estimation command whose parameters are interpreted as log rate ratios, such as logit or logistic for case-control data, or stcox for survival data. It estimates the log of the mean rate ratio, in cases or deaths, between 2 scenarios, 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. This ratio is known as the population unattributable fraction (PUF), and is subtracted from 1 to derive the population attributable fraction (PAF), defined as the proportion of the cases or deaths attributable to living in Scenario 0 instead of Scenario 1. Author: Roger Newson Distribution-Date: 16october2013 Stata-Version: 13 INSTALLATION FILES (click here to install) punafcc.ado punafcc_p.ado punafcc.sthlp -------------------------------------------------------------------------------------- (click here to return to the previous screen) -------------------------------------------------------------------------------------- package regpar from http://www.imperial.ac.uk/nhli/r.newson/stata13 -------------------------------------------------------------------------------------- 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: 16october2013 Stata-Version: 13 INSTALLATION FILES (click here to install) regpar.ado regpar_p.ado regpar.sthlp -------------------------------------------------------------------------------------- (click here to return to the previous screen) -------------------------------------------------------------------------------------- package margprev from http://www.imperial.ac.uk/nhli/r.newson/stata13 -------------------------------------------------------------------------------------- TITLE margprev: Marginal prevalences from binary regression models DESCRIPTION/AUTHOR(S) margprev calculates confidence intervals for marginal prevalences, also known as scenario proportions. margprev can be used after an estimation command whose predicted values are interpreted as conditional proportions, such as logit, logistic, probit, or glm. It estimates a marginal prevalence for a scenario ("Scenario 1"), in which one or more predictor variables may be assumed to be set to particular values, and any other predictor variables in the model are assumed to remain the same. Author: Roger Newson Distribution-Date: 16october2013 Stata-Version: 13 INSTALLATION FILES (click here to install) margprev.ado margprev_p.ado margprev.sthlp -------------------------------------------------------------------------------------- (click here to return to the previous screen) * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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