|
Unfortunately, I don't use SPSS mixed to do my mixed models analyses but
I can tell you that the AR(1) structure is probably okay here. You can
test it but running a model that doesn't include any predictors other
than time and then comparing the variance covariance structure's impact
on fit. Once you have settled on the best var-cov structure, then youcan
test your hypothese about the fixed effects. How many hosptitals do you
have in the sample? If the number is small, say less than 20-30, then
you may not have enough variance in the hospitals to include it as a
random effect.
Usually, you only need to specify one level of the model as random. The
residual will represent the other effect.
Paul R. Swank, Ph.D. Professor
Director of Reseach
Children's Learning Institute
University of Texas Health Science Center-Houston
-----Original Message-----
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of
Erik Langer Madsen
Sent: Wednesday, July 11, 2007 6:59 AM
To: SPSSX-L@LISTSERV.UGA.EDU
Subject: Weight loss study.Mixed linear model. Help appreciated
I am currently investigating a dataset including 68 subjects originating
from 3 different hospitals, who's had measured various parameters
including bodyweight at 5 different timepoints during a three year
weight loss study. The subjects can be characterized by different
between subjects factors: Gender: m/f, Diabetes: yes/no, treatment:
active/placebo and covariates such as age. Given that the data are
correlated (repeated
measurements) and that some of the parameters that I wish to analyze
have missing values for some individuals I'd prefer using the mixed
linear model but I'm troubled by various outputs with different p-values
depending on how many fixed factors I add and which covariance structure
I use.
At present I use the ar1 covariance structure and time, gender, diabetes
and treatment as fixed factors. Including interactions between time and
the other fixed factors.
1. The AR1 structure seems to be my best choice given that the
correlation should be stronger regarding timepoints closer to each
other. But does anyone have arguments for choosing unstructured or
compound symmetry instead?
2.I ought to add hospital as a random factor but then I get a non
positive hessian matrix. Any explanation for this problem or suggestions
for a solution would be appreciated.
3. Should I add subject (individuals) as a random factor under variance
components?
4. When gradually increasing the number of fixed factors into the model
then the p-values for levels and changes seems to differ a lot.
How should I test the validity of the model (Wald ? Overlapping of
confidence intervals?
Best regards
Erik Langer Madsen
erik.langer@ki.au.dk
Phd-student
Aarhus Denmark
| 
