Thanks as always to Kit Baum, new versions of the packages esetran and kmest 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 esetran and kmest are described as below on my website. The new esetran now still transforms in observations where the input standard error variable is missing and the input estimate variable is nonmissing, in which case the estimate is transformed and the standard error stays missing. It has also harmonized the definition of transform(loglog) to harmonize with glm, which affects the estimate but not the standard error. (The loglog transform is the one used by sts list and sts generate.) The new kmest now saves the Greenwood standard errors and the corresponding diagonal matrix of variances in new estimation results e(greenwood_se) and e(greenwood_Vdiag), respectively, enabling kmest to be parmested with estimates and Greenwood standard errors which can then be transformed to delta-Greenwood standard errors using esetran. (This overlaps with the functionality of sts list and sts generate, but adds the option of saving to a frame.)
Best wishes
Roger
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package esetran from https://www.rogernewsonresources.org.uk/stata10
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TITLE
esetran: Transforming estimates and standard errors in parmest resultssets
DESCRIPTION/AUTHOR(S)
esetran is designed for use in parmest resultssets, which have one
observation per estimated parameter and data on parameter estimates.
It inputs 2 user-specified variables, containing the estimates and the
standard errors, and replaces them with the estimates and standard
errors of the same parameters after a user-specified transformation,
promoting their storage types to double precision if necessary.
Parameter values at the boundaries of the parameter range (such as the
logit of 1 or 0 or the hyperbolic arctangent of 1 or -1) are set to
sensible non-missing boundary values. The user can then use the
parmcip option of the parmest package to recalculate the t- and
z-statistics, symmetric confidence limits and P-values for the
transformed parameters, and use endpoint transformations on the
estimates and confidence limits to produce asymmetric confidence
intervals for back-transformed parameters. This practice is
especially useful if the user has produced symmetric confidence
intervals for scenario proportions and their differences, using
margins after a logistic regression, and needs to replace them with
asymmetric confidence intervals, which are more likely to have the
correct coverage probability.
Author: Roger Newson
Distribution-Date: 13july2025
Stata-Version: 10
INSTALLATION FILES (click here to install)
esetran.ado
esetran.sthlp
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(click here to return to the previous screen)
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package kmest from https://www.rogernewsonresources.org.uk/stata16
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TITLE
kmest: Compute Kaplan-Meier survival probabilities and/or percentiles as estimation results
DESCRIPTION/AUTHOR(S)
kmest is intended for use in a survival time dataset set up by stset.
It computes Kaplan-Meier survival probabilities (as computed by sts
generate) for a list of times (sorted in ascending order), and/or
Kaplan-meier percentiles for a list of percents (srted in ascending
order), and saves them as estimation results, without a variance
matrix. kmest is intended for use with the bootstrap prefix, or
possibly with the jackknife prefix, to create confidence intervals for
the Kaplan-Meier survival probabilities and/or percentiles, possibly
allowing for clustering and/or sampling-probability weighting.
Alternatively, kmest can be used with the SSC packages parmest and
esetran to compute Greenwood confidence intervals, or delta-Greenwood
confidence intervals using a variety of Normalizing transforms.
Author: Roger Newson
Distribution-Date: 14 July 2025
Stata-Version: 16
INSTALLATION FILES (click here to install)
kmest.ado
kmest_p.ado
kmest.sthlp
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(click here to return to the previous screen)
The packages esetran and kmest are described as below on my website. The new esetran now still transforms in observations where the input standard error variable is missing and the input estimate variable is nonmissing, in which case the estimate is transformed and the standard error stays missing. It has also harmonized the definition of transform(loglog) to harmonize with glm, which affects the estimate but not the standard error. (The loglog transform is the one used by sts list and sts generate.) The new kmest now saves the Greenwood standard errors and the corresponding diagonal matrix of variances in new estimation results e(greenwood_se) and e(greenwood_Vdiag), respectively, enabling kmest to be parmested with estimates and Greenwood standard errors which can then be transformed to delta-Greenwood standard errors using esetran. (This overlaps with the functionality of sts list and sts generate, but adds the option of saving to a frame.)
Best wishes
Roger
-----------------------------------------------------------------------------------------------------------------------------------------------------------
package esetran from https://www.rogernewsonresources.org.uk/stata10
-----------------------------------------------------------------------------------------------------------------------------------------------------------
TITLE
esetran: Transforming estimates and standard errors in parmest resultssets
DESCRIPTION/AUTHOR(S)
esetran is designed for use in parmest resultssets, which have one
observation per estimated parameter and data on parameter estimates.
It inputs 2 user-specified variables, containing the estimates and the
standard errors, and replaces them with the estimates and standard
errors of the same parameters after a user-specified transformation,
promoting their storage types to double precision if necessary.
Parameter values at the boundaries of the parameter range (such as the
logit of 1 or 0 or the hyperbolic arctangent of 1 or -1) are set to
sensible non-missing boundary values. The user can then use the
parmcip option of the parmest package to recalculate the t- and
z-statistics, symmetric confidence limits and P-values for the
transformed parameters, and use endpoint transformations on the
estimates and confidence limits to produce asymmetric confidence
intervals for back-transformed parameters. This practice is
especially useful if the user has produced symmetric confidence
intervals for scenario proportions and their differences, using
margins after a logistic regression, and needs to replace them with
asymmetric confidence intervals, which are more likely to have the
correct coverage probability.
Author: Roger Newson
Distribution-Date: 13july2025
Stata-Version: 10
INSTALLATION FILES (click here to install)
esetran.ado
esetran.sthlp
-----------------------------------------------------------------------------------------------------------------------------------------------------------
(click here to return to the previous screen)
-----------------------------------------------------------------------------------------------------------------------------------------------------------
package kmest from https://www.rogernewsonresources.org.uk/stata16
-----------------------------------------------------------------------------------------------------------------------------------------------------------
TITLE
kmest: Compute Kaplan-Meier survival probabilities and/or percentiles as estimation results
DESCRIPTION/AUTHOR(S)
kmest is intended for use in a survival time dataset set up by stset.
It computes Kaplan-Meier survival probabilities (as computed by sts
generate) for a list of times (sorted in ascending order), and/or
Kaplan-meier percentiles for a list of percents (srted in ascending
order), and saves them as estimation results, without a variance
matrix. kmest is intended for use with the bootstrap prefix, or
possibly with the jackknife prefix, to create confidence intervals for
the Kaplan-Meier survival probabilities and/or percentiles, possibly
allowing for clustering and/or sampling-probability weighting.
Alternatively, kmest can be used with the SSC packages parmest and
esetran to compute Greenwood confidence intervals, or delta-Greenwood
confidence intervals using a variety of Normalizing transforms.
Author: Roger Newson
Distribution-Date: 14 July 2025
Stata-Version: 16
INSTALLATION FILES (click here to install)
kmest.ado
kmest_p.ado
kmest.sthlp
-----------------------------------------------------------------------------------------------------------------------------------------------------------
(click here to return to the previous screen)
