Tasting

Analysis of Prostate Data

This analysis of prostate data includes baseline demographics, staging information and treatment administered.

Median age

61.85 years IQR 56 - 66

prostate %>% 
  mutate(aa = factor(aa, levels = c(0,1), 
                     labels = c("White", "African-American"))) %>% 
  mutate(fam_hx = factor(fam_hx, levels = c(0,1), 
      labels = c("No Family History", "FHx of Prostate Cancer"))) ->
prostate_factors

prostate %>% 
  select(age, p_vol, preop_psa, aa, fam_hx) %>% 
  group_by(aa, fam_hx) %>% 
  summarize(across(age:preop_psa, ~ mean(.x, na.rm=TRUE)))

## `summarise()` has grouped output by 'aa'. You can override using the `.groups`
## argument.

## # A tibble: 4 × 5
## # Groups:   aa [2]
##      aa fam_hx   age p_vol preop_psa
##   <dbl>  <dbl> <dbl> <dbl>     <dbl>
## 1     0      0  61.8  56.9      8.06
## 2     0      1  59.5  57.3      7.22
## 3     1      0  60.7  54.3      9.90
## 4     1      1  60.1  51.4      8.71

This data suggests family history of prostate cancer is associated with a lower mean age. Black people have a higher pre-op PSA irrespective of FHx status.

Plot of race versus family history

You can also embed plots, for example:

ggplot(prostate_factors) + 
  aes(x = p_vol, y = preop_psa, col = aa) + 
  geom_point() +
  geom_smooth(method = "lm") +
  facet_grid(aa ~ fam_hx) +
  labs(x = 'Prostate Volume', y = "Preoperative PSA",
       title = 'Relationship Between Prostate Volume and Preop PSA,\nSubdivided by Family History and Race') +
  theme(legend.position = "bottom")

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 11 rows containing non-finite outside the scale range
## (`stat_smooth()`).

## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).

Baesed on this plot, In absence of FHx, as prostate volume increases, pre-op PSA increases, but this is only true for non black people. This same trend was not observed in black people.

Statistical testing

Null hypothesis - There is no significant difference in pre-operative PSA levels according to race.

prostate_factors %>% 
  t_test(formula = preop_psa ~ aa,
         detailed = TRUE)

## # A tibble: 1 × 15
##   estimate estimate1 estimate2 .y.    group1 group2    n1    n2 statistic      p
## *    <dbl>     <dbl>     <dbl> <chr>  <chr>  <chr>  <int> <int>     <dbl>  <dbl>
## 1    -1.89      7.86      9.75 preop… White  Afric…   259    54     -1.96 0.0534
## # ℹ 5 more variables: df <dbl>, conf.low <dbl>, conf.high <dbl>, method <chr>,
## #   alternative <chr>

A t test demonstrates a non signficiant different in mean pre operative PSA between race groups, therefore we are unable to rejec the null hypothesis.