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st: New versions of -punaf-, -punafcc-, -regpar-, -margprev- and -marglmean- on SSC
From
"Roger B. Newson" <[email protected]>
To
"[email protected]" <[email protected]>
Subject
st: New versions of -punaf-, -punafcc-, -regpar-, -margprev- and -marglmean- on SSC
Date
Wed, 25 Jan 2012 13:18:31 +0000
Thanks as always to Kit Baum, new versions of the packages -punaf-,
-punafcc-, -regpar-, =margprev- and -marglmean- are now available for
download from SSC. In Stata, use the -ssc- command to do this, or
-adoupdate- if you already have old versions of these packages.
The packages -punaf-, -punafcc-, -regpar-, -margprev- and -marglmean-
are described as below on my website, and form a suite of front-end
programs, using -margins- and -nlcom-, for calculating confidence
intervals for scenario-comparison statistics using Normalizing and
variance-stabilizing transformations. These scenario-comparison
statistics are population unattributable and attributable fractions for
ctoss-sectional, cohort, case-control and survival data, population
unattributable and attributable risks for cross-sectional and cohort
data, scenario marginal prevalences for binary outcomes, and scenario
marginal arithmetic means for non-negative outcomes. The new versions
add an option -force-, with the same function as the option of the same
name for -margins-. (I have also removed an unnecessary line throw from
the top of the output of -punaf-, -punafcc- and -regpar-.)
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: [email protected]
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 punaf from http://www.imperial.ac.uk/nhli/r.newson/stata12
---------------------------------------------------------------------------
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: 24january2012
Stata-Version: 12
INSTALLATION FILES (click here to install)
punaf.ado
punaf_p.ado
punaf.sthlp
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(click here to return to the previous screen)
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package punafcc from http://fmwww.bc.edu/RePEc/bocode/p
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TITLE
'PUNAFCC': module to compute population attributable fractions
for case-control and survival studies
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.
KW: confidence intervals
KW: population attributable fractions
KW: case-control studies
KW: survival studies
Requires: Stata version 12
Distribution-Date: 20120124
Author: Roger Newson, National Heart and Lung Institute at
Imperial College London
Support: email [email protected]
INSTALLATION FILES (click here to install)
punafcc.ado
punafcc_p.ado
punafcc.sthlp
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(click here to return to the previous screen)
---------------------------------------------------------------------------
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: 24january2012
Stata-Version: 12
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/stata12
---------------------------------------------------------------------------
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: 24january2012
Stata-Version: 12
INSTALLATION FILES (click here to install)
margprev.ado
margprev_p.ado
margprev.sthlp
---------------------------------------------------------------------------
(click here to return to the previous screen)
---------------------------------------------------------------------------
package marglmean from http://www.imperial.ac.uk/nhli/r.newson/stata12
---------------------------------------------------------------------------
TITLE
marglmean: Marginal log means from regression models
DESCRIPTION/AUTHOR(S)
marglmean calculates symmetric confidence intervals for log
marginal means (also known as log scenario means), and
asymmetric confidence intervals for the marginal means
themselves. marglmean can be used after an estimation
command whose predicted values are interpreted as positive
conditional arithmetic means of non-negative-valued outcome
variables, such as logit, logistic, probit, poisson, or glm
with most non-Normal distributional families. It can
estimate a marginal mean for a scenario ("Scenario 1"), in
which one or more exposure 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: 24january2012
Stata-Version: 12
INSTALLATION FILES (click here to install)
marglmean.ado
marglmean_p.ado
marglmean.sthlp
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(click here to return to the previous screen)
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