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st: Analysis of experiment involving baseline measurements
From
Philip Jones <[email protected]>
To
[email protected]
Subject
st: Analysis of experiment involving baseline measurements
Date
Tue, 28 Jun 2011 05:31:28 -0400
Hello,
I am looking for some guidance from Statalisters with respect to analysis of a
randomized trial. Please forgive me in advance for the length of this message!
The trial's intervention is a teaching technique. Participants are randomly
allocated to the new technique (simulation) or the old (didactic group). The
dependent variable is continuous on a scale from 0-24 (the sum of numeric
responses on a questionnaire), and is measured at baseline ('pre') and twice
more (immediately after the intervention and after six weeks: 'post' and
'sixwk').
I have set up my data file as follows (this sample data looks normal in a
fixed-width font):
+---------------------------------+
| group pre post sixwk |
|---------------------------------|
1. | didactic 14 24 17 |
2. | simulation 12 23 22 |
3. | didactic 18 24 19 |
4. | simulation 16 23 23 |
5. | didactic 10 20 16 |
|---------------------------------|
6. | simulation 9 21 24 |
7. | didactic 14 22 20 |
8. | simulation 14 23 16 |
+---------------------------------+
According to Senn (1), if there were only one measurement post-baseline
measurement, the preferred way of analyzing this data would be to compare the
two groups on the 'post' score while adjusting for the baseline values using
ANCOVA. However, I have two post-baseline measurements, and I'm wondering how I
might be able to take the timing of the measurements into account using a mixed
model, or whether I should bother getting that technical and instead perform two
separate comparisons:
1) 'post' result adjusted for baseline
2) 'sixwk' result adjusted for baseline
These analyses are easy enough:
regress sixwk i.group c.pre
Has anyone got an opinion as to whether or not I should go down the road of a
mixed ANOVA to analyze these data?
Many thanks in advance for any advice/assistance!
Phil
(1) Senn S, Statistical Issues in Drug Development, 2nd ed.
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