Repeated Measures for MLQ

#Loading the dataset that has been reset into a long version
data.test4 <- read.csv("/Volumes/TOSHIBA EXT/Dropbox/ADULT STUDY/adult_study011615.csv")
#Creating a new variable that is the mean of all positive purpose MLQ questions
data.test4$MLQP <- apply(data.test4[, c("MLQ1" ,"MLQ4", "MLQ5", "MLQ6")], 1, mean, na.rm = TRUE)
library(reshape2); library(car)

## Warning: package 'car' was built under R version 3.1.2

data <- data.test4[,c("ID", "GROUP", "wave", "MLQP")]
data <- dcast(data, ID + GROUP ~ wave, mean, value.var = "MLQP")
data[,3:5] <- apply(data[,3:5],2,function(x) recode(x, "NaN = NA") )

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", "MLQP", "WAVE")

Drop the cases where participants did not complete the intervention complety

#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)

Load the psych package

library(psych)

## 
## Attaching package: 'psych'
## 
## The following object is masked from 'package:car':
## 
##     logit

Describe the MLQ 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 4.67 1.21    4.5    4.72 1.11 1.5   7   5.5 -0.35
## MLQP        2 59 5.05 1.14    5.0    5.07 1.11 2.5   7   4.5 -0.21
##          kurtosis   se
## BASELINE    -0.10 0.13
## MLQP        -0.59 0.15
## -------------------------------------------------------- 
## group: 1
##          vars  n mean   sd median trimmed  mad  min max range  skew
## BASELINE    1 88 4.47 1.45    4.5    4.47 1.48 2.00   7  5.00  0.04
## MLQP        2 54 5.67 1.05    6.0    5.74 1.11 3.25   7  3.75 -0.49
##          kurtosis   se
## BASELINE    -0.99 0.16
## MLQP        -0.78 0.14

Create a plot that visualizes MLQ 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(MLQP ~ BASELINE, data=data2)$residual

Plot the residuals to see that they are random

plot(density(residual))# A density plot

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

Checking the different between intervention and control groups residuals. This allows us to control for individual unsystematic differences.

data2$residual <- NA
sel1 <- which(!is.na(data2$MLQP)) 
sel2 <- which(!is.na(data2$BASELINE))
data2$residual[intersect(sel1,sel2)] <- residual
qplot(GROUP, MLQP, data=data2, geom="boxplot")

## Warning: Removed 65 rows containing non-finite values (stat_boxplot).

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).

# Load the nlme package
library(nlme)

Two way repeated measures Graphing the Two-Way Interaction.

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

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

Linear Mixed-Effects Model

Comparing Basline to Wave 2 and 3 by Group.

fullModel <- lme(MLQP ~ 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
##   250.6 269.4 -118.3
## 
## Random effects:
##  Formula: ~1 | ID
##         (Intercept) Residual
## StdDev:      0.5354   0.5331
## 
## Fixed effects: MLQP ~ GROUP * WAVE + BASELINE 
##               Value Std.Error DF t-value p-value
## (Intercept)  1.6704    0.4045 66   4.129  0.0001
## GROUP1       1.0237    0.3592 66   2.850  0.0058
## WAVE         0.1302    0.1558 38   0.835  0.4089
## BASELINE     0.6389    0.0684 66   9.340  0.0000
## GROUP1:WAVE -0.1935    0.2279 38  -0.849  0.4013
##  Correlation: 
##             (Intr) GROUP1 WAVE   BASELI
## GROUP1      -0.418                     
## WAVE        -0.500  0.596              
## BASELINE    -0.798  0.012 -0.038       
## GROUP1:WAVE  0.334 -0.878 -0.684  0.035
## 
## Standardized Within-Group Residuals:
##      Min       Q1      Med       Q3      Max 
## -2.81113 -0.40934  0.03359  0.38633  1.99723 
## 
## Number of Observations: 109
## Number of Groups: 69

Table with P-values

	Value	Std.Error	DF	t-value	p-value
(Intercept)	1.6704	0.4045	66.0000	4.1292	0.0001
GROUP1	1.0237	0.3592	66.0000	2.8502	0.0058
WAVE	0.1302	0.1558	38.0000	0.8352	0.4089
BASELINE	0.6389	0.0684	66.0000	9.3404	0.0000
GROUP1:WAVE	-0.1935	0.2279	38.0000	-0.8488	0.4013

``` Table with confidence intervals

	est.	lower	upper
(Intercept)	1.6704	0.8815	2.4594
GROUP1	1.0237	0.3232	1.7241
WAVE	0.1302	-0.1780	0.4383
BASELINE	0.6389	0.5055	0.7723
GROUP1:WAVE	-0.1935	-0.6442	0.2572