[R-sig-ME] Linear mixed model query
Etn bot
etnbot1 at gmail.com
Thu Sep 24 16:06:51 CEST 2015
Hi all,
My study looks at allergy levels of skin patches from patients and readings
(repeated 5 times) are measured over 4 time points
I need to determine if the allergy level for skin patch changes over time
(e.g. if allergy level from skin patch 1 for patient 1 at time 0 is
different from allergy level for skin patch 1 for patient 1 at time 1 etc.)
I do not want to see the difference between skin patch 1 and skin patch
2....
using package lmer:
model<-lmer(allergy_level ~ time +(time|patient/patch))
Results from this model indicate that time is not significant - the average
patient allergy level for individual skin patches does not change over
time:
Random effects:
Groups Name Variance Std.Dev. Corr
ID:patch (Intercept) 17.4109 4.1726
time1 2.7109 1.6465 -0.30
time2 3.0082 1.7344 -0.26 0.60
time3 5.7643 2.4009 -0.35 0.15 0.54
patch (Intercept) 19.1576 4.3769
time1 0.2103 0.4586 -0.56
time2 0.4372 0.6612 -0.94 0.48
time3 0.5895 0.7678 -0.48 0.96 0.49
Residual 4.9467 2.2241
Number of obs: 2956, groups: ID:patch, 149; patch, 16
Fixed effects:
Estimate Std. Error t value
(Intercept) 6.44763 1.15028 5.605
time1 -0.01907 0.21237 -0.090
time2 -0.03172 0.24759 -0.128
time3 -0.01124 0.29940 -0.038
model1: Force ~ 1 + (1 + time | patch/ID)
model2: Force ~ time + (1 + time | patch/ID)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
model11 22 14281 14413 -7118.5 14237
model12 25 14287 14437 -7118.4 14237 0.0208 3 0.9992
I have extracted the random coefficients from model 1:
ranef(model1)
$`ID:patch`
(Intercept) time1 time2 time3
1:11 5.9845070 0.34088535 0.431998708 1.590906238
1:12 5.1236456 -0.03178611 -0.149784278 -0.116150278
1:13 6.3746877 -0.76853294 -0.550037715 0.842518786
:
:
However, I need to be able to tell if there is a significant difference for
individual patches for individual patients over time
e.g.
If I run individual linear regression on patient 1 for skin patch 1,
results show that that time is significant:
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.0800 0.6523 27.717 5.95e-15 ***
time1 0.3600 0.9225 0.390 0.701502
time2 1.2400 0.9225 1.344 0.197641
time3 -4.3400 0.9225 -4.705 0.000239 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.459 on 16 degrees of freedom
Multiple R-squared: 0.7323, Adjusted R-squared: 0.6821
F-statistic: 14.59 on 3 and 16 DF, p-value: 7.679e-05
If I run individual regression models for each skin patch for each patient,
this will result in a large number of models as I have There are 16 skin
patches per patient. (10 patients in total) 5 readings are taken at each of
the 4 time points.
I thought linear mixed models would be an appropriate method to answer my
question (I need to be able to tell if there is a significant difference
for individual patches for individual patients over time).
Any advice is greatly appreciated,
Many thanks
Etn
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