hope this helps. spencer graves
Karl Knoblick wrote:
Hallo!
I have data of the following design: NSubj were measured at Baseline (visit 1) and at 3
following time points (visit 2, visit 3, visit 4).
There is or is not a treatment.
Most interesting is the question if there is a difference in treatment between the results of visit 4 and baseline. (The other time points are also of interest.) The level of significance is alpha=0.0179 (because of an interim analysis).
My questions:
1) I prefer calculating s model with the differences to baseline as Y: Y ~ treat*visit + Error(treat:id) where treat is the treatment and id is the subject's id Other possibilities are: a) Y0 ~ treat*visit + v1 + Error(treat:id) where Y0 are the results (visit 2 to visit 4) and v1 is the result of visit 1 as covariate b) Calculating a t-test with the results of visit 4 and visit 1 What's best?
2) When calculating a model (ANOVA) I have to
calculate the treatment effect (easy with coef(...)).
But how can I get the standard errors, or more exactly
the 98.21% confidence
intervals? - lm() does not work with an Error()-term in the
formula
- using lme(), I can not reproduce results e.g. of an
example of a text book ((Bortz,
Statistik f�r Sozialwissenschaftler, p.412, 3.
Auflage, see at the very end)
Here what I have done (with random data):
# Random generation of data NSubj<-30 # No. of subjects set.seed(1234) id<-c(1:NSubj) # ID of subjects treat<-runif(NSubj, min=0, max=1) > 0.5 # Treatment v1<-runif(NSubj, min=0, max=1) # Result visit 1 Baseline) v2<-runif(NSubj, min=0, max=1) # Result visit 2 v3<-runif(NSubj, min=0, max=1) # Result visit 3 v4<-runif(NSubj, min=0, max=1) # Result visit 4
# Making the data frame Y<-c(v2-v1,v3-v1,v4-v1) # Taking the differences to baseline (visit 1) id<-as.factor(rep(id, 3)) treat<-as.factor(rep(treat, 3)) visit<-as.factor(rep(2:4, each=NSubj)) df<-data.frame(id, treat, visit, Y)
# Analysis df.aov <- aov(Y ~ treat*visit + Error(treat:id), data=df) summary(df.aov) coef(df.aov)
How can I get the SE?
Hope somebody can help!
Karl
Example (Bortz, Statistik f�r Sozialwissenschaftler,
p.412, 3. Auflage), two-factorial
analysis of variance with repeated measurements:
Subject b1 b2 b3 al 1 56 52 48 al 2 57 54 46 al 3 55 51 51 al 4 58 51 50 al 5 54 53 46 a2 1 54 50 49 a2 2 53 49 48 a2 3 56 48 52 a2 4 52 52 50 a2 5 55 51 46 a3 1 57 49 50 a3 2 55 51 47 a3 3 56 48 51 a3 4 58 50 48 a3 5 58 46 52
The factors are A (a1,a2,a3) and B (b1,b2,b3); 5 subjects. Factor A is kind of training (creativity) for the subjacts and factor B is before, during and after training.
The results of the example:
Source SS df sigma2 F A 9.9 2 4.95 3.87 Subject in sample 15.4 12 1.28 between subjects 25.3 14 B 370.7 2 185.35 44.03 (**) AxB 45.6 4 11.4 2.71 BxSubject 101.0 24 4.21 within subjects 517.3 30 Total 542.6 44
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