Summary of results

This document illustrates the querying and application of results derived and stored following the analysis results data model (ARDM). The tabs contain examples rendering the results from a descriptive, safety, and survival analysis standard into plots and tables.

# Load libraries
library(tidyverse)
library(DBI)
library(RSQLite)
library(here)
library(scales)
library(reactable)

# Make sure path is relative
here::i_am("ARDM/applications/summary.Rmd")

# Load files necessary to create the tables and plots
source(here::here("ARDM", "applications", "plots.R"))
source(here::here("ARDM", "applications", "tables.R"))

# Open connection to the database
connection <- dbConnect(RSQLite::SQLite(), dbname = here::here("ARDM", "database", "ardm.sqlite"))

Descriptive Statistics

Demographics

# Search for the data and include the treatment names
data <- dbGetQuery(connection, "SELECT * FROM descriptive_demographics_continuous
                                LEFT JOIN 
                                  (SELECT DISTINCT parameter_id, name FROM demographics_per_subject) 
                                  AS newtable1
                                USING 
                                (parameter_id)
                                LEFT JOIN 
                                  (SELECT treatment_id, treatment FROM treatments) AS newtable2 
                                USING 
                                (treatment_id)")

# Update margins
par(mar = c(4, 4, .1, .2))

# Plot the results
plot_boxplot(data)

# Search for the data and include the treatment names
data <- dbGetQuery(connection, "SELECT * FROM descriptive_demographics_categorical
                                LEFT JOIN 
                                  (SELECT DISTINCT parameter_id, name FROM demographics_per_subject) 
                                  AS newtable1
                                USING 
                                (parameter_id)
                                LEFT JOIN 
                                  (SELECT treatment_id, treatment FROM treatments) AS newtable2 
                                USING 
                                (treatment_id)")

# Update margins
par(mar = c(4, 4, .1, .2))

# Plot the results
plot_dotplot(data)

Baselines

# Search for the data and include the treatment names
data <- dbGetQuery(connection, "SELECT * FROM descriptive_baselines_continuous
                                LEFT JOIN 
                                  (SELECT DISTINCT parameter_id, name FROM baselines_per_subject) 
                                  AS newtable1
                                USING 
                                (parameter_id)
                                LEFT JOIN 
                                  (SELECT treatment_id, treatment FROM treatments) AS newtable2 
                                USING 
                                (treatment_id)")

# Update margins 
par(mar = c(4, 4, .1, .2))

plots <- purrr::map(unique(data$name), # Get the parameters
                function(x) {filtered_data <- data %>% dplyr::filter(name == x) # Filter by parameter
                            plot_boxplot(filtered_data) # Plot the results
                            }
                )

Safety Analysis

# Search for the data and include the treatment names
data <- dbGetQuery(connection, "SELECT * FROM crude_incidence
                                LEFT JOIN 
                                  (SELECT treatment_id, treatment FROM treatments) AS newtable 
                                USING (treatment_id)")
# Select the data to show                           
selected_data <- data %>%
  dplyr::select(study_id, treatment, SOC, PT, N_PT, P_PT) %>% 
  dplyr::distinct()

# Show the results table
create_AE_table(selected_data)

Survival Analysis

# Search for the data 
data <- dbGetQuery(connection, "SELECT * FROM survival_analysis")

# Update margins 
par(mar = c(4, 4, .1, .2))

# Plot the results
plot_km(data)

# Search for the data 
data <- dbGetQuery(connection, "SELECT * FROM survival_analysis")

# Update margins 
par(mar = c(4, 4, .1, .2))

# Optional: filter the data  
filtered_data <- data %>% 
  dplyr::filter(!strata  %in% "treatment=Xanomeline low dose")

# Plot the filtered results
plot_km(filtered_data)