Generalized least squares for trend estimation of summarized dose–response data
Nicola Orsini
Karolinska Institutet
Stockholm, Sweden
[email protected]
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Rino Bellocco
Karolinska Institutet
Stockholm, Sweden
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Sander Greenland
UCLA School of Public Health
Los Angeles, CA
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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.
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
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