Pam_SyracuseWorkshops4VI_ControlSkills

Control Skill Scores

SubjectID	W1	W2	W3	W4	W5
100	24	24	NA	NA	NA
103	NA	18	21	NA	32
106	29	30	NA	NA	NA
107	28	34	37	NA	24
122	36	42	NA	24	NA
125	40	NA	33	NA	NA
130	9	NA	NA	NA	NA
135	42	NA	NA	NA	NA
137	21	26	NA	NA	NA
139	NA	35	26	NA	20
143	39	41	37	NA	38
160	42	42	NA	NA	33
172	22	34	40	NA	24

Converting dataframe from wide to long form

Control Skill Scores for 13 Subjects over 5 Workshops

SubjectID	workshop	score
100	W1	24
103	W1	NA
106	W1	29
107	W1	28
122	W1	36
125	W1	40
130	W1	9
135	W1	42
137	W1	21
139	W1	NA
143	W1	39
160	W1	42
172	W1	22
100	W2	24
103	W2	18
106	W2	30
107	W2	34
122	W2	42
125	W2	NA
130	W2	NA
135	W2	NA
137	W2	26
139	W2	35
143	W2	41
160	W2	42
172	W2	34
100	W3	NA
103	W3	21
106	W3	NA
107	W3	37
122	W3	NA
125	W3	33
130	W3	NA
135	W3	NA
137	W3	NA
139	W3	26
143	W3	37
160	W3	NA
172	W3	40
100	W4	NA
103	W4	NA
106	W4	NA
107	W4	NA
122	W4	24
125	W4	NA
130	W4	NA
135	W4	NA
137	W4	NA
139	W4	NA
143	W4	NA
160	W4	NA
172	W4	NA
100	W5	NA
103	W5	32
106	W5	NA
107	W5	24
122	W5	NA
125	W5	NA
130	W5	NA
135	W5	NA
137	W5	NA
139	W5	20
143	W5	38
160	W5	33
172	W5	24

Missing Control Skill Score Summary

SubjectID	missing
100	3
103	2
106	3
107	1
122	2
125	3
130	4
135	4
137	3
139	2
143	1
160	2
172	1

• Using smoother to see subject specific trend

p1 + geom_line() + stat_smooth(aes(group = SubjectID)) + stat_summary(aes(group = SubjectID),
    geom = "point", fun.y = mean, shape = 17, size = 3) 

• Calculating Smoother for each subject

p1 + geom_line() + stat_smooth(aes(group = SubjectID), method = "lm", se = FALSE) +
  stat_summary(aes(group = SubjectID), geom = "point", fun.y = mean, shape = 17, size = 3) 

• Now, we might be interested in estimating the overall trend in the data. One option is to add a line using locally weighted regression (lowess) to ???smooth??? over all the variability and give a sense of the overall or average trend. It just takes one short line of code and is automatically calculated

• Note again, we use group = 1, so the smooth is not calculated separately for each id

• geom_smooth: method=“auto” and size of largest group is <1000, so using loess. Use ‘method = x’ to change the smoothing method.

p1 + geom_line() + stat_smooth(aes(group = 1)) + stat_summary(aes(group = 1),
    geom = "point", fun.y = mean, shape = 17, size = 3) 

• Looks like between worskshop 1 and 3, an intervention/ or something happened, and we wish to fit a piecewise linear model rather than an overall smooth. We can do this by creating a dummy variable (pre/post intervention) and its interaction with time. The only change is a slightly more complex formula.

• The default is y ~ x. I(x > 1) creates a dummy (TRUE/FALSE) variable if x (time in this case) is greater than 1. The aestrick in the formula asks for the main effects and the interaction between x and the dummy variable from x.

• Now we can see that the trend line jumps after W2, and the slope is allowed to change and it seems drastic indicating their is an interaction between time and intervention (if the change appears minimal, it is a suggestion there is not an interaction between our hypothetical intervention and time)

p1 + geom_line() + stat_smooth(aes(group = 1), method = "lm", formula = y ~
    x * I(x > 3), se = FALSE) + stat_summary(aes(group = 1), fun.y = mean, geom = "point",
    shape = 17, size = 3) 

Randomisation based tests along with t test to compare control skill scores with baseline workshop W1

• Preparing the data

• Test code for extracting Complete Cases

CS comparison W1vs W2

## [1] TRUE

## # A tibble: 8 x 3
##   SubjectID    W1    W2
##       <chr> <dbl> <dbl>
## 1       100    24    24
## 2       106    29    30
## 3       107    28    34
## 4       122    36    42
## 5       137    21    26
## 6       143    39    41
## 7       160    42    42
## 8       172    22    34

Complete Cases between W1 and W2

SubjectID	W1	W2
100	24	24
106	29	30
107	28	34
122	36	42
137	21	26
143	39	41
160	42	42
172	22	34

## Classes 'tbl_df', 'tbl' and 'data.frame':    8 obs. of  3 variables:
##  $ SubjectID: chr  "100" "106" "107" "122" ...
##  $ W1       : num  24 29 28 36 21 39 42 22
##  $ W2       : num  24 30 34 42 26 41 42 34

CS Comparison W1 vs. W3

## # A tibble: 4 x 3
##   SubjectID    W1    W3
##       <chr> <dbl> <dbl>
## 1       107    28    37
## 2       125    40    33
## 3       143    39    37
## 4       172    22    40

##   SubjectID               W1              W3       
##  Length:4           Min.   :22.00   Min.   :33.00  
##  Class :character   1st Qu.:26.50   1st Qu.:36.00  
##  Mode  :character   Median :33.50   Median :37.00  
##                     Mean   :32.25   Mean   :36.75  
##                     3rd Qu.:39.25   3rd Qu.:37.75  
##                     Max.   :40.00   Max.   :40.00

CS Comparison W1 vs. W4, not possible as n=1

## # A tibble: 1 x 3
##   SubjectID    W1    W4
##       <chr> <dbl> <dbl>
## 1       122    36    24

CS Comparison W1W5,

## # A tibble: 4 x 3
##   SubjectID    W1    W5
##       <chr> <dbl> <dbl>
## 1       107    28    24
## 2       143    39    38
## 3       160    42    33
## 4       172    22    24

##   SubjectID               W1              W5       
##  Length:4           Min.   :22.00   Min.   :24.00  
##  Class :character   1st Qu.:26.50   1st Qu.:24.00  
##  Mode  :character   Median :33.50   Median :28.50  
##                     Mean   :32.75   Mean   :29.75  
##                     3rd Qu.:39.75   3rd Qu.:34.25  
##                     Max.   :42.00   Max.   :38.00

### Creating Final Table of p-values related to randomisation based test and t-test for paired comparisons

P-values for Randomisation and t-test

	Rand test	ttest
W2 vs. W1	0.016	0.014
W3 vs. W1	0.312	0.240
W4 vs. W1	NA	NA
W5 vs. W1	0.880	0.855