9.11 Solutions

Solution to Exercise 9.10.1:

## Recode
melanoma <- melanoma %>% 
  mutate(sex.factor = factor(sex) %>%          
           fct_recode("Female" = "0",
                      "Male"   = "1") %>% 
           ff_label("Sex"),   
         
         ulcer.factor = factor(ulcer) %>% 
           fct_recode("Present" = "1",
                      "Absent"  = "0") %>% 
           ff_label("Ulcerated tumour"),
         
         age  = ff_label(age,  "Age (years)"),
         year = ff_label(year, "Year"),
         
         status.factor = factor(status) %>% 
           fct_recode("Died melanoma"  = "1",
                      "Alive" = "2",
                      "Died - other" = "3") %>% 
           fct_relevel("Alive") %>% 
           ff_label("Status"),
         
         t_stage.factor = 
           thickness %>% 
           cut(breaks = c(0, 1.0, 2.0, 4.0, 
                          max(thickness, na.rm=TRUE)),
               include.lowest = TRUE)
  )

# Plot
p1 <- melanoma %>% 
  ggplot(aes(x = sex.factor, fill = mort_5yr)) + 
  geom_bar() + 
  theme(legend.position = "none")

p2 <- melanoma %>% 
  ggplot(aes(x = sex.factor, fill = mort_5yr)) + 
  geom_bar(position = "fill") + 
  ylab("proportion")

p1 + p2

Solution to Exercise 9.10.2:

## Recode T-stage first
melanoma <- melanoma %>% 
  mutate(
    t_stage.factor = 
      fct_recode(t_stage.factor,
                 T1 = "[0,1]",
                 T2 = "(1,2]",
                 T3 = "(2,4]",
                 T4 = "(4,17.4]") %>% 
      ff_label("T-stage")
  )

dependent = "sex.factor"
explanatory = c("age", "t_stage.factor", "ulcer.factor")
melanoma %>% 
  summary_factorlist(dependent, explanatory, p = TRUE, na_include = TRUE,
                     cont = "median")

# Men have more T4 tumours and they are more likely to be ulcerated. 

Solution to Exercise 9.10.3:

dependent = "mort_5yr"
explanatory = c("sex.factor", "age", "t_stage.factor", "ulcer.factor")
melanoma %>% 
  finalfit(dependent, explanatory, metrics = TRUE)

# c-statistic = 0.798
# In multivariable model, male vs female OR 1.26 (0.57-2.76, p=0.558).
# No relationship after accounting for T-stage and tumour ulceration. 
# Sex is confounded by these two variables. 

Solution to Exercise 9.10.4:

dependent = "mort_5yr"
explanatory = c("sex.factor", "age", "t_stage.factor", "ulcer.factor")
melanoma %>% 
  or_plot(dependent, explanatory)