Repeated Measures for Sense of Identity

#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 meanAPSI questions

items <- grep("APSI[0-8]", names(data.test4), value=TRUE)
scaleKey <- c(1, 1, 1, 1, 1, -1, 1, 1)
data.test4[,items] <- apply(data.test4[,items], 2, as.numeric)
data.test4$meanAPSI <- scoreItems(scaleKey, items = data.test4[, items], delete = FALSE)$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", "meanAPSI")]
data <- dcast(data, ID + GROUP ~ wave, mean, value.var = "meanAPSI")
data[,3:5] <- apply(data[,3:5],2,function(x) recode(x, "NaN = NA") )

Create new data set with ID Group basline meanAPSI 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", "meanAPSI", "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 meanAPSI 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 3.96 0.63   4.00    4.00 0.56 2.38   5  2.62 -0.48
## meanAPSI    2 59 4.09 0.65   4.12    4.14 0.56 2.25   5  2.75 -0.68
##          kurtosis   se
## BASELINE    -0.17 0.07
## meanAPSI     0.03 0.08
## -------------------------------------------------------- 
## group: 1
##          vars  n mean   sd median trimmed  mad  min max range  skew
## BASELINE    1 88 3.74 0.78   3.69    3.77 0.83 1.62   5  3.38 -0.32
## meanAPSI    2 54 4.31 0.48   4.38    4.33 0.56 3.00   5  2.00 -0.42
##          kurtosis   se
## BASELINE    -0.18 0.08
## meanAPSI    -0.56 0.07

Create a plot that visualizes meanAPSI 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(meanAPSI ~ 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$residual <- NA
sel1 <- which(!is.na(data2$meanAPSI)) 
sel2 <- which(!is.na(data2$BASELINE))
data2$residual[intersect(sel1,sel2)] <- residual
qplot(GROUP, meanAPSI, 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 meanAPSI and the Residuals

# Load the nlme package
library(nlme)
with(data2, boxplot(meanAPSI ~ 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(meanAPSI ~ 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
##   116.1 134.9 -51.05
## 
## Random effects:
##  Formula: ~1 | ID
##         (Intercept) Residual
## StdDev:      0.2831   0.2911
## 
## Fixed effects: meanAPSI ~ GROUP * WAVE + BASELINE 
##               Value Std.Error DF t-value p-value
## (Intercept)  1.5246   0.29720 66   5.130  0.0000
## GROUP1       0.4979   0.19533 66   2.549  0.0131
## WAVE         0.0928   0.08501 38   1.092  0.2818
## BASELINE     0.5917   0.06740 66   8.779  0.0000
## GROUP1:WAVE -0.0866   0.12429 38  -0.697  0.4903
##  Correlation: 
##             (Intr) GROUP1 WAVE   BASELI
## GROUP1      -0.350                     
## WAVE        -0.338  0.594              
## BASELINE    -0.895  0.055 -0.062       
## GROUP1:WAVE  0.222 -0.877 -0.685  0.052
## 
## Standardized Within-Group Residuals:
##      Min       Q1      Med       Q3      Max 
## -2.21221 -0.54771 -0.03645  0.59968  2.44696 
## 
## Number of Observations: 109
## Number of Groups: 69

Table with P-values

Value Std.Error DF t-value p-value
(Intercept) 1.5246 0.2972 66.0000 5.1299 0.0000
GROUP1 0.4979 0.1953 66.0000 2.5493 0.0131
WAVE 0.0928 0.0850 38.0000 1.0917 0.2818
BASELINE 0.5917 0.0674 66.0000 8.7790 0.0000
GROUP1:WAVE -0.0866 0.1243 38.0000 -0.6966 0.4903

``` Table with confidence intervals

est. lower upper
(Intercept) 1.5246 0.9450 2.1042
GROUP1 0.4979 0.1170 0.8789
WAVE 0.0928 -0.0753 0.2609
BASELINE 0.5917 0.4603 0.7232
GROUP1:WAVE -0.0866 -0.3324 0.1592