adapted from formerfda.com

Overview

In this case study, we will look at the results of a clinical trial exploring the effectiveness of a new medication called dimarta on reducing histamine in patients with a disease that leads to chronically high histamine levels. In the study, 300 patients were assigned to one of three different treatment arms. One arm was given a placebo. The other arm was given adiclax – the standard of care for the disease. Finally, the third arm was given dimarta. There were two main measures of interest in the trial: patient’s changes in histamine from the beginning to the end of the trial, and their change in quality of life (measured by self report).

In addition to exploring the effects of the three medications, the researchers are interested in the extent to which three different biomarkers, dw, ms, and np, are correlated with therapeutic outcomes. In other words, to patients that express one or more of these biomarkers have better, or worse, outcomes that those that do not express these biomarkers?

Tasks

A - Getting Setup

  1. Open your bernrbootcamp R project. It should already have the folders 1_Data and 2_Code.

  2. We want to generate an R Markdown document for this case study. In RStudio, click File – New File – R Markdown – Document – Html. Save your markdown file in your main project directory under the name dimarta_casestudy.Rmd.

  3. At the top of the script load the tidyverse package.

  4. From time to time - don’t forget to Knit your code with the Knit-Button.

B - Data I/O

  1. Using read_csv(), load the dimarta_trial.csv, dimarta_demographics.csv, and dimarta_biomarker.csv datasets as three new objects called trial_df, demographics_df, and biomarker_df.
## read in datasets
trial_df <- read_csv("1_Data/dimarta_trial.csv")
demographics_df <- read_csv("1_Data/dimarta_demographics.csv")
biomarker_df <- read_csv("1_Data/dimarta_biomarker.csv")
  1. Get a first impression of the objects you just created by exploring them with a mixture of the View(), head(), names(), and str() functions. Were they all loaded correctly?
trial_df
# A tibble: 300 x 6
   PatientID   arm histamine_start histamine_end qol_start qol_end
   <chr>     <dbl>           <dbl>         <dbl>     <dbl>   <dbl>
 1 txdjezeo      1            58.6          67.0         3       3
 2 htxfjlxk      3            36.1          28.0         3       4
 3 vkdqhyez      1            57.7          57.3         2       2
 4 dbuvrwfq      3            56.6          57.4         2       3
 5 ydaitaah      2            64.7          67.9         5       7
 6 omhxokdr      1            37.4          41.7         2       2
 7 dsybafny      1            88.4          95.9         4       6
 8 fdfmcoto      2            20.2          18.9         2       2
 9 rwsbykxe      2            48.3          43.0         3       3
10 xocueqqe      3            48.6          55.2         1       1
# … with 290 more rows
demographics_df
# A tibble: 300 x 5
   PatientID   age gender site   diseasestatus
   <chr>     <dbl>  <dbl> <chr>  <chr>        
 1 pkyivajv     36      0 Tokyo  Mid          
 2 dbuvrwfq     39      0 Paris  Late         
 3 jhuztppp     30      0 Tokyo  Mid          
 4 qejexgza     34      1 Tokyo  Late         
 5 cszrjxju     41      1 Tokyo  Late         
 6 uhvgttqh     31      0 London Late         
 7 cflnybdw     45      1 Tokyo  Mid          
 8 igobmmvj     48      0 Tokyo  Late         
 9 lcrtmerg     35      0 London Late         
10 fjjrnsnt     43      1 London Mid          
# … with 290 more rows
biomarker_df
# A tibble: 900 x 3
   PatientID Biomarker BiomarkerStatus
   <chr>     <chr>     <lgl>          
 1 ygazqssv  dw        FALSE          
 2 qosueuyw  ms        FALSE          
 3 bhhykjvw  ms        FALSE          
 4 ifajorty  np        TRUE           
 5 gxnsybdt  ms        FALSE          
 6 igobmmvj  ms        FALSE          
 7 knnzlzun  ms        FALSE          
 8 glzcbmby  ms        FALSE          
 9 gxnsybdt  dw        TRUE           
10 fzrhdpdu  np        FALSE          
# … with 890 more rows

C - Data Wrangling

  1. Change the name of the column arm in the trial_df data to StudyArm.
trial_df <- trial_df %>%
  rename(StudyArm = arm)
  1. Using the table() function, look at the values of the StudyArm column in trial_df. You’ll notice the values are 1, 2, and 3. Using mutate() and case_when() change these values to the appropriate names of the study arms (look at the variable descriptions to see which is which!)
# table(trial_df$StudyArm)

trial_df <- trial_df %>%
  mutate(StudyArm = case_when(
    StudyArm == 1 ~ "placebo",
    StudyArm == 2 ~ "adiclax",
    StudyArm == 3 ~ "dimarta"
  ))
  1. In the demographics_df data, you’ll see that gender is coded as 0 and 1. Using mutate() create a new column in demographics_df called gender_c that shows gender as a string, where 0 = “male”, and 1 = “female”.
demographics_df <- demographics_df %>%
  mutate(gender_c = case_when(
    gender == 0 ~ "male",
    gender == 1 ~ "female"
  ))
  1. Now let’s create a new object called dimarta_df that combines data from trial_df and demographics_df. To do this, use left_join() to combine the trial_df data with the demographics_df data. This will merge the two datasets so you can have the study results and demographic data in the same dataframe. Make sure to assign the result to a new object called dimarta_df
# Create a new dataframe called dimarta_df that contains both trial_df and demographics_df
dimarta_df <- trial_df %>%
  left_join(demographics_df)
  1. You’ll notice that the biomarker_df dataframe is in the ‘long’ format, where each row is a patient’s biomarker result. Making use of the spread() function, create a new dataframe called biomarker_wide_df where each row is a patient, and the results from different biomarkers are in different columns. When you finish, look at biomarker_wide_df to see how it looks!
# Convert biomarker_df to a wide format using spread()
biomarker_wide_df <- biomarker_df %>%
  spread(Biomarker, BiomarkerStatus)
  1. Now, using the left_join function, add the biomarker_wide_df data to the dimarta_df data! Now you should have all of the data in a single dataframe called dimarta_df
dimarta_df <- dimarta_df %>% 
  left_join(biomarker_wide_df)
  1. View dimarta_df to make sure the data look correct! The data should have one row for each patient, and 13 separate columns, including dw, ms, and np
dimarta_df
# A tibble: 300 x 14
   PatientID StudyArm histamine_start histamine_end qol_start qol_end   age
   <chr>     <chr>              <dbl>         <dbl>     <dbl>   <dbl> <dbl>
 1 txdjezeo  placebo             58.6          67.0         3       3    39
 2 htxfjlxk  dimarta             36.1          28.0         3       4    42
 3 vkdqhyez  placebo             57.7          57.3         2       2    47
 4 dbuvrwfq  dimarta             56.6          57.4         2       3    39
 5 ydaitaah  adiclax             64.7          67.9         5       7    35
 6 omhxokdr  placebo             37.4          41.7         2       2    41
 7 dsybafny  placebo             88.4          95.9         4       6    35
 8 fdfmcoto  adiclax             20.2          18.9         2       2    50
 9 rwsbykxe  adiclax             48.3          43.0         3       3    35
10 xocueqqe  dimarta             48.6          55.2         1       1    42
# … with 290 more rows, and 7 more variables: gender <dbl>, site <chr>,
#   diseasestatus <chr>, gender_c <chr>, dw <lgl>, ms <lgl>, np <lgl>
  1. Using the mean() function, calculate the mean age of all patients.
mean(dimarta_df$age)
[1] 39.9
  1. Create a table showing how many male and female patients were in the trial.
dimarta_df %>%
  group_by(gender_c) %>%
  summarise(
    Counts = n()    
  )
# A tibble: 2 x 2
  gender_c Counts
  <chr>     <int>
1 female      140
2 male        160
  1. Now, using similar code, find out how many patients were assigned to each study arm.
dimarta_df %>%
  group_by(StudyArm) %>%
  summarise(
    Counts = n()    
  )
# A tibble: 3 x 2
  StudyArm Counts
  <chr>     <int>
1 adiclax     100
2 dimarta     100
3 placebo     100
  1. Find out how many men and women were assigned to each study arm (Hint: You can use very similar code to what you used above, just add a second grouping variable!)
dimarta_df %>%
  group_by(StudyArm, gender_c) %>%
    mutate(Counts = n())
# A tibble: 300 x 15
# Groups:   StudyArm, gender_c [6]
   PatientID StudyArm histamine_start histamine_end qol_start qol_end   age
   <chr>     <chr>              <dbl>         <dbl>     <dbl>   <dbl> <dbl>
 1 txdjezeo  placebo             58.6          67.0         3       3    39
 2 htxfjlxk  dimarta             36.1          28.0         3       4    42
 3 vkdqhyez  placebo             57.7          57.3         2       2    47
 4 dbuvrwfq  dimarta             56.6          57.4         2       3    39
 5 ydaitaah  adiclax             64.7          67.9         5       7    35
 6 omhxokdr  placebo             37.4          41.7         2       2    41
 7 dsybafny  placebo             88.4          95.9         4       6    35
 8 fdfmcoto  adiclax             20.2          18.9         2       2    50
 9 rwsbykxe  adiclax             48.3          43.0         3       3    35
10 xocueqqe  dimarta             48.6          55.2         1       1    42
# … with 290 more rows, and 8 more variables: gender <dbl>, site <chr>,
#   diseasestatus <chr>, gender_c <chr>, dw <lgl>, ms <lgl>, np <lgl>,
#   Counts <int>
  1. Add a new column to the dimarta_df data called histamine_change that shows the change in patient’s histamine levels from the start to the end of the trial (Hint: use mutate() and just subtract histamine_start from histamine_end!)
dimarta_df <- dimarta_df %>%
  mutate(
    histamine_change = histamine_end - histamine_start
  )
  1. Add a new column to dimarta_df called qol_change that shows the change in patient’s quality of life.
dimarta_df <- dimarta_df %>%
  mutate(
    qol_change = qol_end - qol_start
  )

# Look at result
dimarta_df %>% 
  select(qol_change)
# A tibble: 300 x 1
   qol_change
        <dbl>
 1          0
 2          1
 3          0
 4          1
 5          2
 6          0
 7          2
 8          0
 9          0
10          0
# … with 290 more rows
  1. Calculate the percentage of patients who tested positive for each of the three biomarkers (Hint: If you calculate the mean() of a logical vector, you will get the percentage of TRUE values!)
# Calculate percent of patients with positive biomarkers

dimarta_df %>%
  summarise(
    dw_mean = mean(dw),
    ms_percent = mean(ms),
    np_percent = mean(np)
  )
# A tibble: 1 x 3
  dw_mean ms_percent np_percent
    <dbl>      <dbl>      <dbl>
1   0.257       0.19      0.233
  1. Were there different distributions of age in the different trial sites? To answer this, separately calculate the mean and standard deviations of patient ages in each site. (Hint: group the data by site, then calculate two separate summary statistics: age_mean = mean(age), and age_sd = sd(age).
# Calculate the mean change in histamine for each study site
dimarta_df %>%
  group_by(site) %>%
  summarise(
    age_mean = mean(age),
    age_sd = sd(age)
  )
# A tibble: 3 x 3
  site   age_mean age_sd
  <chr>     <dbl>  <dbl>
1 London     39.9   5.86
2 Paris      39.8   4.84
3 Tokyo      40.1   4.50
  1. Calculate the mean change in histamine results separately for each study site
# Calculate the mean change in histamine for each study site
dimarta_df %>%
  group_by(site) %>%
  summarise(
    histamine_change_mean = mean(histamine_change, na.rm = TRUE)
  )
# A tibble: 3 x 2
  site   histamine_change_mean
  <chr>                  <dbl>
1 London                  1.99
2 Paris                   2.29
3 Tokyo                   1.29
  1. Calculate the mean change in histamine results (histamine_change) for each study arm. Which study arm had a largest decrease in histamine?
# Calculate the mean change in histamine for each study site
dimarta_df %>%
  group_by(StudyArm) %>%
  summarise(
    histamine_change_mean = mean(histamine_change, na.rm = TRUE)
  )
# A tibble: 3 x 2
  StudyArm histamine_change_mean
  <chr>                    <dbl>
1 adiclax                  0.210
2 dimarta                  2.51 
3 placebo                  2.90
  1. Calculate the mean change in quality of life (qol_change) for each study arm. Do the results match what you found with the histamine results?
# Calculate the mean change in histamine for each study site
dimarta_df %>%
  group_by(StudyArm) %>%
  summarise(
    qol_change_mean = mean(qol_change, na.rm = TRUE)
  )
# A tibble: 3 x 2
  StudyArm qol_change_mean
  <chr>              <dbl>
1 adiclax             0.06
2 dimarta             0.01
3 placebo            -0.15

D - Plotting

  1. Create boxplots showing the relationship between study arm and histamine change.
ggplot(data = dimarta_df,
       mapping = aes(x = StudyArm, 
                     y = histamine_change)) +
  geom_boxplot() +
  labs(title = "Histamine change",
       subtitle = "Dimarta",
       caption = "I love R!")

  1. Try using geom_jitter() to add the raw points to the plot
ggplot(data = dimarta_df,
       mapping = aes(x = StudyArm, 
                     y = histamine_change)) +
  geom_boxplot() + 
  geom_jitter(width = .1) +
  labs(title = "Histamine change",
       subtitle = "Dimarta",
       caption = "I love R!")

  1. Create the same plot as above, but instead of analysing study arm, try analysing gender. (Tip! convert gender to a factor with factor(gender)) What do you find? Did one gender have better histamine improvements than the other?
ggplot(data = dimarta_df,
       mapping = aes(x = factor(gender), 
                     y = histamine_change)) +
  geom_boxplot() +
  geom_jitter(width = .1) +
  labs(title = "Histamine change",
       subtitle = "Dimarta",
       caption = "I love R!",
       x = "Gender")

  1. Now create the same plot but show both gender and study arm in the same plot. One way to do this would be to color the points by gender!
ggplot(data = dimarta_df,
       mapping = aes(x = StudyArm, 
                     y = histamine_change,
                     col = factor(gender))) +
  geom_boxplot() +
  geom_jitter(width = .1) +
  labs(title = "Histamine change",
       subtitle = "Dimarta",
       caption = "I love R!",
       x = "Gender")

  1. Is there a correlation between patient’s starting and ending histamiine levels? Create a scatterplot with a regression line to find out!
ggplot(data = dimarta_df,
       aes(x = histamine_start, 
           y = histamine_end)) +
  geom_point() + 
  geom_smooth() +
  labs(title = "Histamine start and end correlation",
       caption = "Dimarta")

  1. Now create the same plot as above, but have different colored points for different study arms (but only one regression line).
ggplot(data = dimarta_df,
       aes(x = histamine_start, 
           y = histamine_end,
           col = StudyArm)) +
  geom_point() + 
  geom_smooth(col = "black") +
  labs(title = "Histamine start and end correlation",
       caption = "Dimarta")

  1. Instead of having different study arms as different colored points, create another plot using facet_wrap() to have different study arms in different plotting panels.
ggplot(data = dimarta_df,
       aes(x = histamine_start, 
           y = histamine_end)) +
  geom_point() + 
  geom_smooth(col = "black") +
  facet_wrap(~ StudyArm) +
  labs(title = "Histamine start and end correlation",
       caption = "Dimarta")

Datasets

File Rows Columns Description
dimarta_trial.csv 300 6 Key DIMARTA trial outcomes
dimarta_biomarker.csv 900 3 Biomarker status’ for 3 different biomarkers for each patient.
dimarta_demographics.csv 300 5 Demographic information for each patient

Column Descriptions

dimarta_trial.csv

Table1. dimarta_trial.csv variable description:
Variable Description
PatientID Unique patient id
arm Treatment arm, either 1 = placebo, 2 = adiclax (the standard of treatment), or 3 = dimarta (the target drug)
histamine_start histamine value at the start of the trial
histamine_end histamine value at the end of the trial
qol_start Patient’s rated quality of life at the start of the trial
qol_end Patient’s rated quality of life at the end of the trial

dimarta_demographics.csv

Table2. dimarta_demographics.csv variable description:
Variable Description
PatientID Unique patient id
age Patient age
gender Patient gender, 0 = male, 1 = female
site Site where the clinical trial was conducted
diseasestatus Status of the patient’s disease at start of trial

dimarta_biomarker.csv

Table3. dimarta_biomarker.csv variable description:
Variable Description
PatientID Unique patient id
Biomarker One of three biomarkers: dw, ms, and np
BiomarkerStatus Result of the test for the biomarker.

Functions

Packages

Package Installation
tidyverse install.packages("tidyverse")
haven install.packages("haven")