4 2022 Advanced Bio2 Introduction To GEE
4 2022 Advanced Bio2 Introduction To GEE
4 2022 Advanced Bio2 Introduction To GEE
- Often people would fit a linear model to such data and only then adjust the
standard errors to account for the clustering; the problem is that this post-hoc
approach does not affect the parameter estimates in the model.
-
GEEs are
- A different way to estimate regression coefficients (not based on likelihoods)
- A: You are a doctor. You want to know how much a certain drug will reduce
your patient’s odds of getting a heart attack.
- B: You are a department of health official. You want to know how the number
of people who die of heart attacks would change if everyone at risk took a
certain drug.
g(Y) = 𝛃X + CORR
Parts of a GEE
g(Y) - outcome, related to the systematic part through a link function g()
AND
○ FEV1
○ Age at baseline
○ Height at baseline
Data from 2
Code for fitting this
. xtgee fev age smoking2 smoking3 agebase hbase, i(id), t(wave)
corr(exchangeable)
** please note: usually the order of your individuals with clusters and the type of
your variables matters more than you might wish. Please read documentation
carefully.
“An expected difference in blood pressure comparing smokers to non-smokers of
the same age and weight”
Not
Nice paper - probably read the introduction only - the rest gets fairly technical. Statistical Analysis of
Correlated Data Using Generalized Estimating Equations: An Orientation James A. Hanley, Abdissa
Negassa, Michael D. deB. Edwardes, Janet E. Forrester American Journal of Epidemiology, Volume 157,
Issue 4, 15 February 2003, Pages 364–375, https://academic.oup.com/aje/article/157/4/364/78911