Centre Data

Overview

Histograms, means, and one-way ANOVAs for the CFQ (Total, Forgetfulness, Distractability, False Triggering)

n=88 (only patients with data at pre, post & follow-up included)

CFQ Total

#HISTOGRAMS FOR COUNTS

ggplot(cc.long.triple, aes(cfq_total)) +
  geom_histogram(binwidth = 10) +
  theme_light() +
  facet_wrap(~ visit)

#BOXPLOTS FOR MEANS PER VISIT

ggplot(cc.long.triple, aes(visit, cfq_total)) +
  geom_boxplot() +
  theme_light()

#MEANS PER VISIT

cc.long.triple %>%
  group_by(visit) %>%
  filter(!is.na(cfq_total)) %>%
  summarize(mean(cfq_total))

## # A tibble: 3 × 2
##   visit     `mean(cfq_total)`
##   <chr>                 <dbl>
## 1 follow-up              49.2
## 2 post                   49.3
## 3 pre                    55.3

#ONE WAY ANOVA

cfq.total.anova <- aov(
  cfq_total ~ visit, data = cc.long.triple
)
summary(cfq.total.anova)

##              Df Sum Sq Mean Sq F value Pr(>F)  
## visit         2   2178  1088.8    3.72 0.0255 *
## Residuals   264  77263   292.7                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#POST-HOC TESTS

TukeyHSD(cfq.total.anova)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = cfq_total ~ visit, data = cc.long.triple)
## 
## $visit
##                     diff         lwr       upr     p adj
## post-follow-up 0.1156793 -5.94605518  6.177414 0.9988852
## pre-follow-up  6.0980025  0.07022976 12.125775 0.0466799
## pre-post       5.9823232 -0.06264500 12.027291 0.0531184

CFQ Forget

#HISTOGRAMS FOR COUNTS

ggplot(cc.long.triple, aes(cfq_forget)) +
  geom_histogram(binwidth = 5) +
  theme_light() +
  facet_wrap(~ visit)

#BOXPLOTS FOR MEANS PER VISIT

ggplot(cc.long.triple, aes(visit, cfq_forget)) +
  geom_boxplot() +
  theme_light()

#MEANS PER VISIT

cc.long.triple %>%
  group_by(visit) %>%
  filter(!is.na(cfq_forget)) %>%
  summarize(mean(cfq_forget))

## # A tibble: 3 × 2
##   visit     `mean(cfq_forget)`
##   <chr>                  <dbl>
## 1 follow-up               17.7
## 2 post                    17.4
## 3 pre                     19.9

#ONE WAY ANOVA

cfq.forget.anova <- aov(
  cfq_forget ~ visit, data = cc.long.triple
)
summary(cfq.forget.anova)

##              Df Sum Sq Mean Sq F value  Pr(>F)   
## visit         2    335  167.39   5.126 0.00655 **
## Residuals   264   8622   32.66                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#POST-HOC TESTS

TukeyHSD(cfq.forget.anova)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = cfq_forget ~ visit, data = cc.long.triple)
## 
## $visit
##                      diff        lwr      upr     p adj
## post-follow-up -0.3779367 -2.4028276 1.646954 0.8988725
## pre-follow-up   2.1584270  0.1448807 4.171973 0.0323320
## pre-post        2.5363636  0.5170733 4.555654 0.0093601

CFQ Distract

#HISTOGRAMS FOR COUNTS

ggplot(cc.long.triple, aes(cfq_distract)) +
  geom_histogram(binwidth = 5) +
  theme_light() +
  facet_wrap(~ visit)

#BOXPLOTS FOR MEANS PER VISIT

ggplot(cc.long.triple, aes(visit, cfq_distract)) +
  geom_boxplot() +
  theme_light()

#MEANS PER VISIT

cc.long.triple %>%
  group_by(visit) %>%
  filter(!is.na(cfq_distract)) %>%
  summarize(mean(cfq_distract))

## # A tibble: 3 × 2
##   visit     `mean(cfq_distract)`
##   <chr>                    <dbl>
## 1 follow-up                 16.4
## 2 post                      16.8
## 3 pre                       18.3

#ONE WAY ANOVA

cfq.distract.anova <- aov(
  cfq_distract ~ visit, data = cc.long.triple
)
summary(cfq.distract.anova)

##              Df Sum Sq Mean Sq F value Pr(>F)  
## visit         2    170   84.91   2.491 0.0848 .
## Residuals   264   8999   34.09                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#POST-HOC TESTS

TukeyHSD(cfq.distract.anova)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = cfq_distract ~ visit, data = cc.long.triple)
## 
## $visit
##                     diff        lwr      upr     p adj
## post-follow-up 0.3343973 -1.7343340 2.403129 0.9231341
## pre-follow-up  1.8285893 -0.2285518 3.885730 0.0927725
## pre-post       1.4941919 -0.5688175 3.557201 0.2044034

CFQ False

#HISTOGRAMS FOR COUNTS

ggplot(cc.long.triple, aes(cfq_false)) +
  geom_histogram(binwidth = 5) +
  theme_light() +
  facet_wrap(~ visit)

#BOXPLOTS FOR MEANS PER VISIT

ggplot(cc.long.triple, aes(visit, cfq_false)) +
  geom_boxplot() +
  theme_light()

#MEANS PER VISIT

cc.long.triple %>%
  group_by(visit) %>%
  filter(!is.na(cfq_false)) %>%
  summarize(mean(cfq_false))

## # A tibble: 3 × 2
##   visit     `mean(cfq_false)`
##   <chr>                 <dbl>
## 1 follow-up              12.8
## 2 post                   12.9
## 3 pre                    14.7

#ONE WAY ANOVA

cfq.false.anova <- aov(
  cfq_false ~ visit, data = cc.long.triple
)
summary(cfq.false.anova)

##              Df Sum Sq Mean Sq F value Pr(>F)  
## visit         2    210  105.14   2.826  0.061 .
## Residuals   264   9822   37.21                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#POST-HOC TESTS

TukeyHSD(cfq.false.anova)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = cfq_false ~ visit, data = cc.long.triple)
## 
## $visit
##                     diff        lwr      upr     p adj
## post-follow-up 0.1228294 -2.0384952 2.284154 0.9901572
## pre-follow-up  1.9354557 -0.2137598 4.084671 0.0872303
## pre-post       1.8126263 -0.3427203 3.967973 0.1185962