Repeated Measures for MLQ all (from 1-10)

#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 positive purpose meanmlq questions

items <- grep("MLQ[0-9]*", names(data.test4), value=TRUE)
scaleKey <- c(1,1,1,1,1,1,1,1,-1,1)
data.test4[,items] <- apply(data.test4[,items], 2, as.numeric)
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 4.80 0.60    4.9    4.80 0.59 3.7 6.1   2.4 -0.08
## meanmlq     2 59 4.79 0.73    4.6    4.73 0.59 3.4 7.0   3.6  0.97
##          kurtosis   se
## BASELINE    -0.82 0.06
## meanmlq      1.08 0.09
## -------------------------------------------------------- 
## group: 1
##          vars  n mean   sd median trimmed  mad min max range  skew
## BASELINE    1 88 4.77 0.81   4.70    4.72 0.89 3.4 6.8   3.4  0.45
## meanmlq     2 54 5.20 0.88   5.25    5.21 0.96 3.2 6.8   3.6 -0.19
##          kurtosis   se
## BASELINE    -0.35 0.09
## meanmlq     -0.55 0.12

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

plot of chunk unnamed-chunk-10

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
##   231.8 250.6 -108.9
## 
## Random effects:
##  Formula: ~1 | ID
##         (Intercept) Residual
## StdDev:      0.4781   0.4969
## 
## Fixed effects: meanmlq ~ GROUP * WAVE + BASELINE 
##               Value Std.Error DF t-value p-value
## (Intercept)  2.2957    0.6161 66   3.726  0.0004
## GROUP1       0.3762    0.3323 66   1.132  0.2618
## WAVE        -0.0469    0.1447 38  -0.324  0.7476
## BASELINE     0.5232    0.1173 66   4.461  0.0000
## GROUP1:WAVE  0.0251    0.2118 38   0.119  0.9062
##  Correlation: 
##             (Intr) GROUP1 WAVE   BASELI
## GROUP1      -0.263                     
## WAVE        -0.343  0.601              
## BASELINE    -0.931  0.016  0.020       
## GROUP1:WAVE  0.245 -0.884 -0.684 -0.026
## 
## Standardized Within-Group Residuals:
##      Min       Q1      Med       Q3      Max 
## -2.97494 -0.44132 -0.03043  0.40675  1.94028 
## 
## Number of Observations: 109
## Number of Groups: 69

Table with P-values

Value Std.Error DF t-value p-value
(Intercept) 2.2957 0.6161 66.0000 3.7263 0.0004
GROUP1 0.3762 0.3323 66.0000 1.1319 0.2618
WAVE -0.0469 0.1447 38.0000 -0.3241 0.7476
BASELINE 0.5232 0.1173 66.0000 4.4609 0.0000
GROUP1:WAVE 0.0251 0.2118 38.0000 0.1186 0.9062

``` Table with confidence intervals

est. lower upper
(Intercept) 2.2957 1.0942 3.4972
GROUP1 0.3762 -0.2719 1.0243
WAVE -0.0469 -0.3331 0.2393
BASELINE 0.5232 0.2944 0.7519
GROUP1:WAVE 0.0251 -0.3936 0.4439