Just starting to use lme4.


My study is as follows:



·         Patients at 6 study sites all underwent a surgical procedure

·         All patients have 1-year follow-up.

·         At baseline and at 1 year, patients reported pain on a 0-10
scale.

·         At 1 year, patients reported satisfaction with the procedure
(satisfied vs. not satisfied).



I'm interested in summarizing the change in pain score after adjustment for
age, gender, history of prior back surgery (yes/no) and baseline pain
score. I'm also interested in the proportion who are "satisfied", again
adjusting for potential covariates, such as age, gender, baseline pain
score.  In all analysis, I'd like to include site as a random effect. I
think it's reasonable to assume that there are unmeasured factors clustered
within site that result in variation in underlying changes/rates.



Here's an example of a model I used.  It



fit1 <- lmer(VAS ~ time + cAge + cgender + cplf + (1+time|SiteNum:ID),
data=long)



(where ID = patient ID; SiteNum = site number; cAge = age centered about
mean age; cgender coded as Male=-0.5, Female=+0.5; cplf coded as "prior
lumbar fusion = +0.5, no prior lumbar fusion = -0.5.)



The model works and produces output that makes sense.


My question has to do with interpreting the output, specifically the
standard errors. The mean change in pain is -6.15 points (SE = 0.24).  I'd
like to be able to say, "the mean change in pain, controlling for age,
gender, and prior lumbar fusion, with site as a random effect, was -6.1
points (95% CI X to Y)."  *I'm wondering whether the standard error from
the model output takes into account both the "usual" sampling error as well
as site as a random effect?*


Daniel Cher
djcher@gmail.com
650-269-5763
This email and its attachments are considered confidential.

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