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From |
"Moran, John (NWAHS)" <John.Moran@nwahs.sa.gov.au> |

To |
"'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Gllamm, random effects and splines |

Date |
Thu, 26 Aug 2004 13:24:31 +0930 |

Dear Stas
Thanks for your reply; I agree that the question was not well posed.
However, you did answer my query when you suggested that I "create day^2 variable and add another -eq- statement corresponding to it." This is what I had in mind, but I wished to be a bit more expansive in my ability to detect any "non-linear" trend over time.
What I specifically had in mind was something parallel to the set up in
S-Plus NLME where the formula specifying the particular non-linear model (say,
a one-compartment model with first order absorption) is by default included in
the _
In the patients currently being considered one might expect the fall in
creatinine over time _
Any way, thanks for your suggestion
Best wishes
john
John Moran Department of Intensive Care Medicine The Queen Elizabeth Hospital 28 Woodville Road Woodville SA 5011 Australia Tel: 61 08 8222 6463 Fax 61 08 8222 6045 E-mail: john.moran@nwahs.sa.gov.au
-----Original Message-----
> My question: > > In the absence of the ability to model the "day" correlation structure (for > example AR1) with the current version of gllamm (correct me if I am wrong > here), is it possible to model the (random) slopes using, say, a spline and > if so, what would be the appropriate code (to enable each patient to have > their own (random) "slope" ("spline")).
I am not quite sure your question is well posed. Splines model the trend, or the average, or whatever function of that average is implied by the link function in your generalized linear model. Random effects model the individual variations.
The model you specified postulates that the effect is linear in time, and those effects may differ across individuals. If you want, you can have a more complicated structure, say quadratic over time. With 7 observations per patient, you won't be able to go anywhere beyond that. And with 36 effective observations, estimating anything by maximum likelihood is a heroic effort per se; I would personally doubt whether you have enough variation to even estimate the linear trend reasonably well, unless your patient show a real quick recovery (I know nothing about insult treatment, I'd have to admit :)). If you want to introduce the quadratic term, you would have to create day^2 variable and add another -eq- statement corresponding to it. See whether the variance of that term is significantly different from zero, and keep in mind it has a non-standard distribution. Stata used to have a help file describing it with a reference to the variance components testing in -xtreg-, but I was not able to find it anywhere.
--- Stas Kolenikov -- Ph.D. student in Statistics at UNC-Chapel Hill - http://www.komkon.org/~tacik/ -- Stas.Kolenikov@unc.edu
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