Repeated Measures for MLQ (searching for purpose)

#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

data.test4$meanmlq <- apply(data.test4[, c("MLQ2", "MLQ3", "MLQ7", "MLQ8", "MLQ10")], 1, mean, na.rm = TRUE)


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 4.85 1.69    5.2    5.01 1.78   1   7     6 -0.71
## meanmlq     2 59 4.41 1.66    4.4    4.46 1.78   1   7     6 -0.26
##          kurtosis   se
## BASELINE    -0.41 0.18
## meanmlq     -0.83 0.22
## -------------------------------------------------------- 
## group: 1
##          vars  n mean   sd median trimmed  mad min max range  skew
## BASELINE    1 88 5.03 1.53    5.3    5.23 1.33   1   7     6 -1.05
## meanmlq     2 54 4.65 1.76    5.2    4.78 1.19   1   7     6 -0.75
##          kurtosis   se
## BASELINE     0.55 0.16
## meanmlq     -0.71 0.24

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

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$residual <- NA
sel1 <- which(!is.na(data2$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 the difference between intervention and control groups.

qplot(GROUP, residual, data=data2, geom="boxplot")

## Warning: Removed 69 rows containing non-finite values (stat_boxplot).

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))

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

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
##   362.9 381.7 -174.4
## 
## Random effects:
##  Formula: ~1 | ID
##         (Intercept) Residual
## StdDev:      0.9218   0.8766
## 
## Fixed effects: meanmlq ~ GROUP * WAVE + BASELINE 
##               Value Std.Error DF t-value p-value
## (Intercept)  1.6651    0.5940 66   2.803  0.0066
## GROUP1      -0.0968    0.5956 66  -0.163  0.8713
## WAVE        -0.1719    0.2572 38  -0.668  0.5079
## BASELINE     0.6408    0.0885 66   7.237  0.0000
## GROUP1:WAVE  0.1187    0.3760 38   0.316  0.7540
##  Correlation: 
##             (Intr) GROUP1 WAVE   BASELI
## GROUP1      -0.450                     
## WAVE        -0.625  0.593              
## BASELINE    -0.733 -0.016  0.041       
## GROUP1:WAVE  0.434 -0.872 -0.684 -0.036
## 
## Standardized Within-Group Residuals:
##      Min       Q1      Med       Q3      Max 
## -2.70121 -0.39828  0.01151  0.50969  2.02933 
## 
## Number of Observations: 109
## Number of Groups: 69

Table with P-values

	Value	Std.Error	DF	t-value	p-value
(Intercept)	1.6651	0.5940	66.0000	2.8033	0.0066
GROUP1	-0.0968	0.5956	66.0000	-0.1626	0.8713
WAVE	-0.1719	0.2572	38.0000	-0.6684	0.5079
BASELINE	0.6408	0.0885	66.0000	7.2368	0.0000
GROUP1:WAVE	0.1187	0.3760	38.0000	0.3156	0.7540

``` Table with confidence intervals

	est.	lower	upper
(Intercept)	1.6651	0.5067	2.8235
GROUP1	-0.0968	-1.2584	1.0647
WAVE	-0.1719	-0.6805	0.3367
BASELINE	0.6408	0.4681	0.8135
GROUP1:WAVE	0.1187	-0.6248	0.8621