Home  >>  Archives  >>  Volume 6 Number 1  >>  st0096

The Stata Journal
Volume 6 Number 1: pp. 40-57

Subscribe to the Stata Journal

Generalized least squares for trend estimation of summarized dose–response data

Nicola Orsini
Karolinska Institutet
Stockholm, Sweden
Rino Bellocco
Karolinska Institutet
Stockholm, Sweden
Sander Greenland
UCLA School of Public Health
Los Angeles, CA
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.
Terms of use     View this article (PDF)

View all articles by these authors: Nicola Orsini, Rino Bellocco, Sander Greenland

View all articles with these keywords: glst, dose–response data, generalized least squares, trend, meta-analysis, metaregression

Download citation: BibTeX  RIS

Download citation and abstract: BibTeX  RIS