[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]
Re: st: GEE and lag variables
Thanks a lot, Joseph, for the reference. I'll check it.
Am 14.07.2005 um 12:36 Uhr schrieb Joseph Coveney:
Take a look at Chapter 12 (Time-dependent covariates) in P. J. Diggle,
Heagerty, K-Y. Liang and S. L. Zeger, _Analysis of Longitudinal Data_
Edition. (Oxford: Oxford Univ. Press, 2002), pp. 245-81.
It describes the use of lagged variables in a models fit by GEE for,
example, covariate endogeneity.
According to the same source, at least for cross-sectional analysis,
should use an independence working correlation, unless you can satisfy
"full covariate conditional mean assumption," which the authors
Alex Gamma wrote:
I have longitudinal data from an age cohort of 591 people at 6
over 20 years. I have two psychiatric diagnoses A and B, and I want to
at the question of whether prior occurence of A predicts current A or
vice versa (i.e. whether prior B predicts current B or A). So I
additional variables A_prior and B_prior coding for any prior
diagnosis A or B, and I ran the models
xtgee A A_prior B B_prior some_covariates, i(id) fam(bin) link(logit)
xtgee B B_prior A A_prior some_covariates, i(id) fam(bin) link(logit)
1) A sociologist colleague doubted that it is valid to include this
"any prior occurence" variable, or indeed any lag-variable into the GEE
model, but I don't see any reason as to why not. But just to be sure I
checked with the experts before publishing these models: is he right?
2) If the A_prior and B_prior variables are admissible, is it correct
an exchangeable correlation structure or should I use an independent
structure? (this is what J.W.R. Twisk seems to recommend in "Applied
Longitudinal Data Analysis for Epidemiology", 2003, Cambridge
Alex Gamma, Ph.D.
University Hospital of Psychiatry Zurich
Tel: ++4144 384 2635
Fax: ++4144 384 2446
* For searches and help try: