Article,

Statistical analysis of correlated data using generalized estimating equations: an orientation.

, , , and .
American journal of epidemiology, 157 (4): 364-75 (February 2003)5955<m:linebreak></m:linebreak>LR: 20071114; GR: 5P01/DK45734-05/DK/NIDDK NIH HHS/United States; GR: CA 70269/CA/NCI NIH HHS/United States; GR: R01 DA/KK11598-01/DA/NIDA NIH HHS/United States; JID: 7910653; CIN: Am J Epidemiol. 2003 Aug 1;158(3):289; author reply 289-90. PMID: 12882954; CIN: Am J Epidemiol. 2003 Aug 1;158(3):289; author reply 289-90. PMID: 12882953; ppublish;<m:linebreak></m:linebreak>Anàlisi de dades; GEE; Dades longitudinals.

Abstract

The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other correlated response data, particularly if responses are binary. However, few descriptions of the method are accessible to epidemiologists. In this paper, the authors use small worked examples and one real data set, involving both binary and quantitative response data, to help end-users appreciate the essence of the method. The examples are simple enough to see the behind-the-scenes calculations and the essential role of weighted observations, and they allow nonstatisticians to imagine the calculations involved when the GEE method is applied to more complex multivariate data.

Tags

Users

  • @jkd
  • @jepcastel

Comments and Reviews