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

Author

Listed:
  • Nicola Orsini

    (Karolinska Institutet)

  • Rino Bellocco

    (Karolinska Institutet)

  • Sander Greenland

    (UCLA School of Public Health)

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.

Suggested Citation

  • Nicola Orsini & Rino Bellocco & Sander Greenland, 2006. "Generalized least squares for trend estimation of summarized dose–response data," Stata Journal, StataCorp LP, vol. 6(1), pages 40-57, March.
    • Handle: RePEc:tsj:stataj:v:6:y:2006:i:1:p:40-57
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