Generalized least squares for trend estimation of summarized dose–response data
Nicola Orsini,
Rino Bellocco and
Sander Greenland
Additional contact information
Rino Bellocco: Karolinska Institutet
Sander Greenland: UCLA School of Public Health
Stata Journal, 2006, vol. 6, issue 1, 40-57
Abstract:
This paper presents a command, glst, for trend estimation across different exposure levels for either single or multiple summarized case-control, incidence-rate, and cumulative incidence data. This approach is based on constructing an approximate covariance estimate for the log relative risks and estimating a corrected linear trend using generalized least squares. For trend analysis of multiple studies, glst can estimate fixed- and random-effects metaregression models. Copyright 2006 by StataCorp LP.
Keywords: glst; dose–response data; generalized least squares; trend; meta-analysis; metaregression (search for similar items in EconPapers)
Date: 2006
References: Add references at CitEc
Citations: View citations in EconPapers (42)
Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0096
http://www.stata-journal.com/software/sj6-1/st0096/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:6:y:2006:i:1:p:40-57
Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html
Access Statistics for this article
Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins
More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().