In cukarthik/CancerTreatmentCharacterization: What the package does (short line)

library(devtools)
library(dplyr)
library(ggplot2)
library(tidyr)
library(ggrepel)
library(lubridate)
library(forcats)
library(RColorBrewer)

---

STEP 1: Bringing In the data set and establishing the vectors used in the analyses

---

outputFolder <- params$outputFolder
minCellCount <- params$minCellCount
cohortId <- params$cohortId
cohortName <- params$cohortName
databaseId <- params$databaseId
time_window_for_interventions <- 365

cancerCohortDataTable <- CancerTreatmentCharacterization::getCancerDataSet(params$cohortDatabaseSchema, params$cohortId, params$connection)
writeLines(paste("outputFolder = ", outputFolder, "\n",
      "minCellCount = ", minCellCount, "\n",
      "cohortId = ", cohortId, "\n",
      "cohortName = ", cohortName, "\n",
      "databaseId = ", databaseId, "\n",
       "time_window_for_interventions = ", time_window_for_interventions))

  cancerSpecificVectors <- getVectorsForSpecificCancer(cohortId)
  cancerSpecificVectors

  #function to produce interventions per patient
  augmentedCancerDataSet <- augmentCancerDataSet(cancerCohortDataTable = cancerCohortDataTable,
                                               interventionsVector = cancerSpecificVectors$interventions,
                                               drugVector = cancerSpecificVectors$drugs_vector,
                                               timeWindowForInterventions = time_window_for_interventions)

  #clear out previous run data
  if (file.exists(outputFolder)) {
    unlink(outputFolder, recursive = TRUE)
  } else {
    dir.create(outputFolder, recursive = TRUE)
  }

plot <- examineInterventionsPerYear(augmentedCancerDataSet, cohortName, databaseId, outputFolder, minCellCount)
plot

plot <- examineDxPerYear(augmentedCancerDataSet, cohortName, databaseId, outputFolder, minCellCount)
plot

plot <- examinePercentAgeAtDx(augmentedCancerDataSet, cohortName, databaseId, outputFolder, minCellCount)
plot

plot <- examineAvgNumDrugsByTreatmentClass(augmentedCancerDataSet, cohortName, databaseId, outputFolder, minCellCount)
plot

## All the below plots are based on the index date of each patient and the earliest drug intervention for irrespective of the year the drug (intervention) was taken.

#plot 11a
#calculating the percent of patients who receive adjuvant Endocrine therapy, by year
adjuvant_endrocrine_records <- cancerCohortDataTable %>%
    filter(neoadjuvant == '0', generic_drug_name %in% cancerSpecificVectors$hr_positive_drugs) %>%
    distinct(person_id, dx_year, generic_drug_name, intervention_date) %>%
    arrange(dx_year, person_id, intervention_date) %>%
    group_by(person_id) %>%
    slice(1)
plot <- examinePercentEndocrineForAdjuvantTherapy(adjuvant_endrocrine_records, cohortName, databaseId, outputFolder, minCellCount)
plot

#plot 11a-2-neoadjuvant
neoadjuvant_endrocrine_records <- cancerCohortDataTable %>%
    filter(neoadjuvant == '1', generic_drug_name %in% cancerSpecificVectors$hr_positive_drugs) %>%
    distinct(person_id, dx_year, generic_drug_name, intervention_date) %>%
    arrange(dx_year, person_id, intervention_date) %>%
    group_by(person_id) %>%
    slice(1)
plot <- examinePercentEndocrineForNeoAdjuvantTherapy(neoadjuvant_endrocrine_records, cohortName, databaseId, outputFolder, minCellCount)
plot

#plot 11b
# first line chemotherapy in the adjuvant setting
#same chemotherapy drugs instead of Endocrine therapy
adjuvant_chemo_records <- cancerCohortDataTable %>%
    filter(neoadjuvant == '0', generic_drug_name %in% cancerSpecificVectors$chemo_drugs) %>%
    distinct(person_id, dx_year, generic_drug_name, intervention_date) %>%
    arrange(dx_year, person_id, intervention_date) %>%
    group_by(person_id) %>%
    slice(1)
plot <- examinePercentChemoForAdjuvantTherapy(adjuvant_chemo_records, cohortName, databaseId, outputFolder, minCellCount)
plot

#plot 11c
#same for chemotherapy drugs for neoadjuvant setting instead of adjuvant setting
neoadjuvant_chemo_records <- cancerCohortDataTable %>%
    filter(neoadjuvant == '1', generic_drug_name %in% cancerSpecificVectors$chemo_drugs) %>%
    distinct(person_id, dx_year, generic_drug_name, intervention_date) %>%
    arrange(dx_year, person_id, intervention_date) %>%
    group_by(person_id) %>%
    slice(1)
plot <- examinePercentChemoForNeoAdjuvantTherapy(neoadjuvant_chemo_records, cohortName, databaseId, outputFolder, minCellCount)
plot

#Plot 14
#AntiHER2 treatment variation in adjuvant setting
AntiHER2s <- cancerCohortDataTable %>%
      filter(neoadjuvant == '0', generic_drug_name %in% cancerSpecificVectors$her2_positive_drugs) %>%
      distinct(person_id, dx_year, generic_drug_name, intervention_date) %>%
      arrange(dx_year, person_id, intervention_date) %>%
      group_by(person_id) %>%
      slice(1)

plot <- examineAntiHER2AdjuvantTherapy(AntiHER2s, cohortName, databaseId, outputFolder, minCellCount)
plot

#AntiHER2 treatment variation in adjuvant setting
AntiHER2s <- cancerCohortDataTable %>%
      filter(neoadjuvant == '1', generic_drug_name %in% cancerSpecificVectors$her2_positive_drugs) %>%
      distinct(person_id, dx_year, generic_drug_name, intervention_date) %>%
      arrange(dx_year, person_id, intervention_date) %>%
      group_by(person_id) %>%
      slice(1)

plot <- examineAntiHER2NeoAdjuvantTherapy(AntiHER2s, cohortName, databaseId, outputFolder, minCellCount)
plot

cukarthik/CancerTreatmentCharacterization documentation built on Dec. 19, 2021, 7:03 p.m.