Timeline for ANCOVA to check effect of covariate
Current License: CC BY-SA 3.0
7 events
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| Nov 5, 2012 at 19:07 | comment | added | Peter_F | I need to think about it but will come back to you guys by tomorrow morning. thanks a lot for your help so far! | |
| Nov 5, 2012 at 16:23 | comment | added | Peter Flom | This last question makes no sense. Perhaps you mean "the response is different only because of group"? but that is answered, the answer is "no, age also matters". Model m2 controls for age. | |
| Nov 5, 2012 at 16:21 | comment | added | chl |
I found no interaction (p=0.465), suggesting that the parallel group assumption holds, which can be checked visually (using lattice): xyplot(response ~ age, data=data, groups=group, type=c("p","r")).
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| Nov 5, 2012 at 16:18 | comment | added | Peter Flom |
@chl That's certainly a good idea, but not directly an answer to the original question. but m3 <- with(data, lm(response~as.factor(group)*age)) summary(m3) shows a small and insignificant interaction
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| Nov 5, 2012 at 16:15 | comment | added | chl |
Your model m2 assumes equal slopes for the response ~ age relationship across groups. I would suggest adding a third model which includes an interaction between the grouping factor and the covariate.
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| Nov 5, 2012 at 16:13 | comment | added | Peter_F | The last anova(m1,m2) shows that age affects the effect of group but how can I extract a p-value which says that if all ages would be similar would there still be a difference between controls and patients? I am not sure if with the 3 p-values calculated above one can conclude that the controls are different to the patients ONLY because of the difference in their response? | |
| Nov 5, 2012 at 16:00 | history | answered | Peter Flom | CC BY-SA 3.0 |