Repeated Measures for LIFE SATISFACTION

#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 LIFE SATISFACTION questions
data.test4$LS <- apply(data.test4[, c("LS1","LS2", "LS3", "LS4",  "LS5")], 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", "LS")]
data <- dcast(data, ID + GROUP ~ wave, mean, value.var = "LS")
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", "LS", "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)

Load the psych package

library(psych)

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

Describe the LIFE SATISFACTION 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 kurtosis
## BASELINE    1 86 4.46 1.29    4.6    4.51 1.19 1.6   7   5.4 -0.3    -0.46
## LS          2 59 4.68 1.39    5.0    4.78 0.89 1.0   7   6.0 -0.8     0.19
##            se
## BASELINE 0.14
## LS       0.18
## -------------------------------------------------------- 
## group: 1
##          vars  n mean   sd median trimmed  mad min max range  skew
## BASELINE    1 88 4.63 1.37    4.9    4.69 1.33 1.6 6.8   5.2 -0.41
## LS          2 54 5.33 0.91    5.6    5.45 0.59 2.2 6.6   4.4 -1.48
##          kurtosis   se
## BASELINE    -0.69 0.15
## LS           2.34 0.12

Create a plot that visualizes LIFE SATISFACTION 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(LS ~ 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$residual <- NA
sel1 <- which(!is.na(data2$LS)) 
sel2 <- which(!is.na(data2$BASELINE))
data2$residual[intersect(sel1,sel2)] <- residual
qplot(GROUP, LS, 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(LS ~ WAVE + GROUP))

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

Linear Mixed-Effects Model

Comparing Basline to Wave 2 and 3 by Group.

fullModel <- lme(LS ~ 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
##   285.3 304.1 -135.6
## 
## Random effects:
##  Formula: ~1 | ID
##         (Intercept) Residual
## StdDev:      0.7556   0.5452
## 
## Fixed effects: LS ~ GROUP * WAVE + BASELINE 
##               Value Std.Error DF t-value p-value
## (Intercept)  1.8977    0.4445 66   4.269  0.0001
## GROUP1       0.8493    0.3954 66   2.148  0.0354
## WAVE         0.0861    0.1633 38   0.527  0.6010
## BASELINE     0.5622    0.0813 66   6.918  0.0000
## GROUP1:WAVE -0.1411    0.2381 38  -0.592  0.5571
##  Correlation: 
##             (Intr) GROUP1 WAVE   BASELI
## GROUP1      -0.337                     
## WAVE        -0.458  0.569              
## BASELINE    -0.799 -0.087 -0.055       
## GROUP1:WAVE  0.320 -0.832 -0.686  0.031
## 
## Standardized Within-Group Residuals:
##       Min        Q1       Med        Q3       Max 
## -3.416279 -0.327527  0.002786  0.285883  1.985580 
## 
## Number of Observations: 109
## Number of Groups: 69

Table with P-values

	Value	Std.Error	DF	t-value	p-value
(Intercept)	1.8977	0.4445	66.0000	4.2691	0.0001
GROUP1	0.8493	0.3954	66.0000	2.1479	0.0354
WAVE	0.0861	0.1633	38.0000	0.5274	0.6010
BASELINE	0.5622	0.0813	66.0000	6.9182	0.0000
GROUP1:WAVE	-0.1411	0.2381	38.0000	-0.5924	0.5571

``` Table with confidence intervals

	est.	lower	upper
(Intercept)	1.8977	1.0308	2.7646
GROUP1	0.8493	0.0781	1.6204
WAVE	0.0861	-0.2369	0.4092
BASELINE	0.5622	0.4037	0.7207
GROUP1:WAVE	-0.1411	-0.6119	0.3298