[R] Repeated measures lme or anova
John Coulthard
bahhab at hotmail.com
Fri Jul 4 15:24:34 CEST 2008
Hi
As I can't find an example of my data structure I'd like some advice on which is the most appropriate test for significant effects. If I should be using either lme or anova, is the relevant example below the best/correct way to do the test?
The Data...
2 groups of patients (5 in GroupA, 7 in GroupB)
3 short acting drugs, (I'm not concerned with residual effects from the previous test effecting the current test)
Each patient is given all 8 possible combinations of the 3 drugs (8 measurements from each subject = repeated measures)
The dependent effects are metabolite levels in the blood, a continuous variable but often skewed towards 0.1 which is the minimum detectable level.
I'm look for individual and combined effects of Group and Drug.
There are ~400 metabolites which I intend to test independently. (I know that will leave me with a multiple testing issue)
What I've worked out for lme is
> summary(test.lme <- lme(Value ~ Group*Drug1*Drug2*Drug3 - 1, test,random = ~1|Patient))
> anova(test.lme)
and for anova is
> summary(aov(Value~(Group*Drug1*Drug2*Drug3)+Error(Patient/(Drug1*Drug2*Drug3)),test))
The full structure of the 'test' data table for one metabolite is below.
Thanks for your time and any thoughts you may have.
Ann
> test
Patient Group Drug1 Drug2 Drug3 Value
1 1 A 0 0 0 446.70
2 1 A 0 0 1 0.10
3 1 A 0 1 1 0.10
4 1 A 0 1 0 328.20
5 1 A 1 0 0 0.10
6 1 A 1 0 1 0.10
7 1 A 1 1 1 0.10
8 1 A 1 1 0 0.10
9 2 B 0 0 0 0.10
10 2 B 0 0 1 69.93
11 2 B 0 1 0 878.30
12 2 B 0 1 1 0.10
13 2 B 1 0 1 0.10
14 2 B 1 0 0 0.10
15 2 B 1 1 1 0.10
16 2 B 1 1 0 0.10
17 3 A 0 0 0 0.10
18 3 A 0 0 1 0.10
19 3 A 0 1 0 0.10
20 3 A 0 1 1 0.10
21 3 A 1 0 1 0.10
22 3 A 1 0 0 0.10
23 3 A 1 1 0 0.10
24 3 A 1 1 1 0.10
25 4 B 0 0 1 0.10
26 4 B 0 0 0 688.50
27 4 B 0 1 0 541.00
28 4 B 0 1 1 0.10
29 4 B 1 0 0 0.10
30 4 B 1 0 1 0.10
31 4 B 1 1 1 0.10
32 4 B 1 1 0 0.10
33 5 B 0 0 0 0.10
34 5 B 0 0 1 0.10
35 5 B 0 1 0 541.60
36 5 B 0 1 1 0.10
37 5 B 1 0 0 0.10
38 5 B 1 0 1 0.10
39 5 B 1 1 0 0.10
40 5 B 1 1 1 0.10
41 6 B 0 0 1 58.29
42 6 B 0 0 0 353.60
43 6 B 0 1 0 523.30
44 6 B 0 1 1 0.10
45 6 B 1 0 0 0.10
46 6 B 1 0 1 0.10
47 6 B 1 1 0 0.10
48 6 B 1 1 1 0.10
49 7 A 0 0 0 351.03
50 7 A 0 0 1 0.10
51 7 A 0 1 1 0.10
52 7 A 0 1 0 0.10
53 7 A 1 0 0 0.10
54 7 A 1 0 1 0.10
55 7 A 1 1 1 0.10
56 7 A 1 1 0 0.10
57 8 A 0 0 0 299.80
58 8 A 0 0 1 0.10
59 8 A 0 1 0 0.10
60 8 A 0 1 1 0.10
61 8 A 1 0 1 0.10
62 8 A 1 0 0 0.10
63 8 A 1 1 0 0.10
64 8 A 1 1 1 0.10
65 9 B 0 0 0 355.50
66 9 B 0 0 1 0.10
67 9 B 0 1 0 737.90
68 9 B 0 1 1 322.60
69 9 B 1 0 0 0.10
70 9 B 1 0 1 0.10
71 9 B 1 1 0 0.10
72 9 B 1 1 1 0.10
73 10 A 0 0 1 462.90
74 10 A 0 0 0 657.50
75 10 A 0 1 0 82.13
76 10 A 0 1 1 0.10
77 10 A 1 0 1 0.10
78 10 A 1 0 0 0.10
79 10 A 1 1 1 0.10
80 10 A 1 1 0 0.10
81 11 B 0 0 0 386.70
82 11 B 0 0 1 279.40
83 11 B 0 1 1 407.30
84 11 B 0 1 0 485.20
85 11 B 1 0 1 0.10
86 11 B 1 0 0 0.10
87 11 B 1 1 0 0.10
88 11 B 1 1 1 0.10
89 12 B 0 0 1 0.10
90 12 B 0 0 0 705.10
91 12 B 0 1 0 706.20
92 12 B 0 1 1 450.20
93 12 B 1 0 1 0.10
94 12 B 1 0 0 0.10
95 12 B 1 1 0 0.10
96 12 B 1 1 1 0.10
>
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