Hi John,
1. I do not know why you remove the intercept in the lme model, but
keep it in the aov model.
2. The distributional assumptions are shot --- you can't run any sort
of normal model with these data. You might consider some sort of
binomial (metabolite detected vs. not detected).
Hank
On Jul 4, 2008, at 9:24 AM, John Coulthard wrote:
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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