· You should avoid random-intercepts-and-slopes model with time. Such combo results in error-covariance structure that may be inappropriate.
· To find best fit/analysis: you need to conduct several analysis and then select one with lowest -2LL.
· For selection of an appropriate form of the residual covar matrix, fit a factorial model with fixed effects only (no random) and with an unstructured covar matrix.
· To find best covar structure for 2 models with same fixed effects you test if there is a significant change in -2LL.
Max.
From: NomiW [via SPSSX Discussion] [mailto:[hidden email]]
Sent: 2013-Nov-16 13:02
To: MaxJasper
Subject: Repeated measures in large data set
I have a date set with approximately 1,000,000 people. Medication usage was
recorded monthly throughout one calendar year (i.e. each person has 12 time
points). The variables are numeric and refer to dosage.
I'm interested in comparing use across time, between two different regions and
three different groups. I've run Repeated Measures models with factors and
interactions. Everything is significant because the n is so large. Is there a
better way to do this? The differences between months are very small but all
pairwise comparisons are significant. How do I know which are meaningful?
(I'm particularly interested in comparing one month to the preceding and
following months).
Thanks!
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