Repeated Measures MLQ (all Purpose Questions including negative question reverse coded)

#Loading the dataset that has been reset into a long version
data.test4 <- read.csv("/Volumes/TOSHIBA EXT/Dropbox/ADULT STUDY/adult_study011615.csv")
# Load the psych package
library(psych)

Creating a new variable that is the mean of all purpose meanmlq questions

items <- c("MLQ1" ,"MLQ4", "MLQ5", "MLQ6", "MLQ9")
scaleKey <- c(1, 1, 1,1,-1)
data.test4$meanmlq  <- scoreItems(scaleKey, items=data.test4[,items], delete=FALSE)$score

library(reshape2); library(car)
## Warning: package 'car' was built under R version 3.1.2
## 
## Attaching package: 'car'
## 
## The following object is masked from 'package:psych':
## 
##     logit
data <- data.test4[,c("ID", "GROUP", "wave", "meanmlq")]
data <- dcast(data, ID + GROUP ~ wave, mean, value.var = "meanmlq")
data[,3:5] <- apply(data[,3:5],2,function(x) recode(x, "NaN = NA") )

Create new data set with ID Group baseline meanmlq and wave so that we have Baseline, time 1 and 2 to compare to

data2 <- as.data.frame(mapply(c,data[,1:4], data[,c(1:3,5)]))
data2$wave <- rep(1:2, each=89)
names(data2) <- c("ID", "GROUP", "BASELINE", "meanmlq", "WAVE")

Drop the cases where participants did not complete the intervention completely

#data2 <- data2[-c(which(data2$GROUP ==2)),]

Intention to treat model (ITT) where we keep the cases who dropped out and did not complete the study (http://en.wikipedia.org/wiki/Intention-to-treat_analysis).

data2[which(data2$GROUP ==2), "GROUP"] <- 1

For lme to work GROUP and ID need to be seen as factors

data2$GROUP <-as.factor(data2$GROUP)
data2$ID <-as.factor(data2$ID)

Describe the meanmlq variable by the GROUP variable

describeBy(data2[,3:4], group = data2$GROUP)
## group: 0
##          vars  n mean   sd median trimmed  mad min max range  skew
## BASELINE    1 86 4.75 1.27    4.8    4.82 1.19 1.4   7   5.6 -0.43
## meanmlq     2 59 5.17 1.15    5.4    5.22 1.19 2.4   7   4.6 -0.45
##          kurtosis   se
## BASELINE    -0.20 0.14
## meanmlq     -0.38 0.15
## -------------------------------------------------------- 
## group: 1
##          vars  n mean   sd median trimmed  mad min max range  skew
## BASELINE    1 88 4.50 1.47    4.4    4.50 1.63 1.8   7   5.2  0.05
## meanmlq     2 54 5.76 1.03    6.0    5.84 0.89 3.8   7   3.2 -0.61
##          kurtosis   se
## BASELINE    -1.05 0.16
## meanmlq     -0.82 0.14

Create a plot that visualizes meanmlq variable by the GROUP variable

library(ggplot2)
## 
## Attaching package: 'ggplot2'
## 
## The following object is masked from 'package:psych':
## 
##     %+%

Take a look at the residuals

residual <- lm(meanmlq ~ BASELINE, data=data2)$residual

Plot the residuals to see that they are random

plot(density(residual))# A density plot

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qqnorm(residual) # A quantile normal plot to checking normality
qqline(residual)

plot of chunk unnamed-chunk-10 Checking the different between intervention and control groups residuals. This allows us to control for individual unsystematic differences.

data2$residual <- NA
sel1 <- which(!is.na(data2$meanmlq)) 
sel2 <- which(!is.na(data2$BASELINE))
data2$residual[intersect(sel1,sel2)] <- residual
qplot(GROUP, meanmlq, data=data2, geom="boxplot")
## Warning: Removed 65 rows containing non-finite values (stat_boxplot).

plot of chunk unnamed-chunk-11 Plot of the difference between intervention and control groups.

qplot(GROUP, residual, data=data2, geom="boxplot")
## Warning: Removed 69 rows containing non-finite values (stat_boxplot).

plot of chunk unnamed-chunk-12 Two way repeated measures ======================================================== Graphing the Two-Way Interaction. Both meanmlq and the Residuals

# Load the nlme package
library(nlme)
with(data2, boxplot(meanmlq ~ WAVE + GROUP))

plot of chunk unnamed-chunk-13

with(data2, boxplot(residual ~ WAVE + GROUP))

plot of chunk unnamed-chunk-13

Linear Mixed-Effects Model

Comparing Basline to Wave 2 and 3 by Group.

fullModel <- lme(meanmlq ~ GROUP * WAVE + BASELINE, random = ~1 | ID, data = data2, method = "ML", na.action = "na.omit")
Results

Explanation of significance:

We asses the significance of our models by comparing them from the baseline model using the anova() function.
(Intercept): Where everything is 0
GROUP1: Is there a difference between group. If it is significant than there is a difference and the treatment had an effect.
WAVE: Asseses whether the effects gets bigger beteen time 2 and 3 (does not have to be significant)
BASELINE: Should not be significant. If it is then it shows that there is a difference between groups before the treatment.
GROUP1:WAVE: If this is significant then it means that the effect was either fleeting or it happened after the treatment i.e. between time 2 and 3.

summary(fullModel)
## Linear mixed-effects model fit by maximum likelihood
##  Data: data2 
##     AIC   BIC logLik
##   240.2 259.1 -113.1
## 
## Random effects:
##  Formula: ~1 | ID
##         (Intercept) Residual
## StdDev:      0.5322   0.4951
## 
## Fixed effects: meanmlq ~ GROUP * WAVE + BASELINE 
##               Value Std.Error DF t-value p-value
## (Intercept)  1.6902    0.3918 66   4.314  0.0001
## GROUP1       0.8796    0.3380 66   2.602  0.0114
## WAVE         0.0773    0.1455 38   0.532  0.5980
## BASELINE     0.6589    0.0654 66  10.071  0.0000
## GROUP1:WAVE -0.0842    0.2126 38  -0.396  0.6941
##  Correlation: 
##             (Intr) GROUP1 WAVE   BASELI
## GROUP1      -0.426                     
## WAVE        -0.484  0.590              
## BASELINE    -0.811  0.037 -0.032       
## GROUP1:WAVE  0.333 -0.869 -0.684  0.021
## 
## Standardized Within-Group Residuals:
##     Min      Q1     Med      Q3     Max 
## -2.4086 -0.4441  0.1101  0.3570  2.0486 
## 
## Number of Observations: 109
## Number of Groups: 69

Table with P-values

Value Std.Error DF t-value p-value
(Intercept) 1.6902 0.3918 66.0000 4.3136 0.0001
GROUP1 0.8796 0.3380 66.0000 2.6023 0.0114
WAVE 0.0773 0.1455 38.0000 0.5317 0.5980
BASELINE 0.6589 0.0654 66.0000 10.0708 0.0000
GROUP1:WAVE -0.0842 0.2126 38.0000 -0.3963 0.6941

``` Table with confidence intervals

est. lower upper
(Intercept) 1.6902 0.9260 2.4543
GROUP1 0.8796 0.2204 1.5388
WAVE 0.0773 -0.2103 0.3650
BASELINE 0.6589 0.5313 0.7865
GROUP1:WAVE -0.0842 -0.5046 0.3361