data_process.R

######################################

# Processes data from cohort extract
######################################

## Packages
library('tidyverse')
library('lubridate')
library('here')
library('gt')
library('gtsummary')
library('arrow')
library('reshape2')

## Import custom user functions
source(here::here("lib", "functions", "fct_case_when.R"))
source(here::here("lib", "functions", "define_covid_hosp_admissions.R"))
source(here::here("lib", "functions", "define_allcause_hosp_admissions.R"))
source(here::here("lib", "functions", "define_status_and_fu_all.R"))
source(here::here("lib", "functions", "define_status_and_fu_primary.R"))
source(here::here("lib", "functions", "define_status_and_fu_secondary.R"))

## Print session info to metadata log file
sessionInfo()

## Print variable names
cat("#### read in data extract ####\n")

data_extract <- read_csv(
  here::here("output", "input.csv.gz"),
  col_types = cols_only(
   
    # Identifier
    patient_id = col_integer(),
    
    # POPULATION ----
    age = col_integer(),
    sex = col_character(),
    ethnicity = col_character(),
    imdQ5 = col_character(),
    region_nhs = col_character(),
    stp = col_factor(),
    rural_urban = col_character(),
    
    # MAIN ELIGIBILITY - FIRST POSITIVE SARS-CoV-2 TEST IN PERIOD ----
    covid_test_positive_date = col_date(format = "%Y-%m-%d"),
    
    # TREATMENT - NEUTRALISING MONOCLONAL ANTIBODIES OR ANTIVIRALS ----
    paxlovid_covid_therapeutics = col_date(format = "%Y-%m-%d"),
    sotrovimab_covid_therapeutics = col_date(format = "%Y-%m-%d"),
    remdesivir_covid_therapeutics = col_date(format = "%Y-%m-%d"),
    molnupiravir_covid_therapeutics = col_date(format = "%Y-%m-%d"),
    casirivimab_covid_therapeutics = col_date(format = "%Y-%m-%d"),
    date_treated = col_date(format = "%Y-%m-%d"),
    
    # PREVIOUS TREATMENT - NEUTRALISING MONOCLONAL ANTIBODIES OR ANTIVIRALS ----
    
    # OVERALL ELIGIBILITY CRITERIA VARIABLES ----
    symptomatic_covid_test = col_character(),
    covid_symptoms_snomed = col_date(format = "%Y-%m-%d"),
    pregnancy = col_logical(),
    
    # CENSORING ----
    death_date = col_date(format = "%Y-%m-%d"),
    dereg_date = col_date(format = "%Y-%m-%d"),
    
    # HIGH RISK GROUPS ----
    high_risk_cohort_covid_therapeutics = col_factor(),
    high_risk_group = col_logical(),
    huntingtons_disease_nhsd = col_logical() , 
    myasthenia_gravis_nhsd = col_logical() , 
    motor_neurone_disease_nhsd = col_logical() , 
    multiple_sclerosis_nhsd = col_logical() , 
    solid_organ_transplant_nhsd = col_logical(), 
    hiv_aids_nhsd = col_logical(), 
    immunosupression_nhsd = col_logical(), 
    imid_nhsd = col_logical(), 
    liver_disease_nhsd = col_logical(), 
    ckd_stage_5_nhsd = col_logical(), 
    haematological_disease_nhsd = col_logical(), 
    cancer_opensafely_snomed = col_logical(), 
    downs_syndrome_nhsd  = col_logical(), 

    # CLINICAL/DEMOGRAPHIC COVARIATES ----
    diabetes = col_logical(),
    bmi = col_character(),
    smoking_status = col_character(),
    copd = col_logical(),
    dialysis = col_logical(),
    cancer = col_logical(),
    lung_cancer = col_logical(),
    haem_cancer = col_logical(),
    
    # VACCINATION ----
    vaccination_status = col_factor(),
    date_most_recent_cov_vac = col_date(format = "%Y-%m-%d"),
    pfizer_most_recent_cov_vac = col_logical(),
    az_most_recent_cov_vac = col_logical(),
    moderna_most_recent_cov_vac = col_logical(),
    
    # VARIANT ----
    sgtf = col_factor(),
    variant = col_factor(),
    
    # OUTCOMES ----
    # covid specific
    covid_hosp_admission_date0 = col_date(format = "%Y-%m-%d"),
    covid_hosp_admission_date1 = col_date(format = "%Y-%m-%d"),
    covid_hosp_admission_date2 = col_date(format = "%Y-%m-%d"),
    covid_hosp_admission_date3 = col_date(format = "%Y-%m-%d"),
    covid_hosp_admission_date4 = col_date(format = "%Y-%m-%d"),
    covid_hosp_admission_date5 = col_date(format = "%Y-%m-%d"),
    covid_hosp_admission_date6 = col_date(format = "%Y-%m-%d"),
    covid_hosp_admission_first_date7_27 = col_date(format = "%Y-%m-%d"),
    covid_hosp_discharge_first_date0_7 = col_date(format = "%Y-%m-%d"),
    covid_hosp_date_mabs_procedure = col_date(format = "%Y-%m-%d"),
    # all cause
    allcause_hosp_admission_date0 = col_date(format = "%Y-%m-%d"),
    allcause_hosp_admission_date1 = col_date(format = "%Y-%m-%d"),
    allcause_hosp_admission_date2 = col_date(format = "%Y-%m-%d"),
    allcause_hosp_admission_date3 = col_date(format = "%Y-%m-%d"),
    allcause_hosp_admission_date4 = col_date(format = "%Y-%m-%d"),
    allcause_hosp_admission_date5 = col_date(format = "%Y-%m-%d"),
    allcause_hosp_admission_date6 = col_date(format = "%Y-%m-%d"),
    allcause_hosp_admission_first_date7_27 = col_date(format = "%Y-%m-%d"),
    allcause_hosp_discharge_first_date0_7 = col_date(format = "%Y-%m-%d"),
    allcause_hosp_date_mabs_procedure = col_date(format = "%Y-%m-%d"),
    # death
    died_ons_covid_any_date = col_date(format = "%Y-%m-%d")
  ),
)

# data cleaning
cat("#### data cleaning ####\n")

## Format columns (i.e, set factor levels)
data_processed <- data_extract %>%
  mutate(
    # Cinic/demo variables -----
    ageband = cut(
      age,
      breaks = c(18, 40, 60, 80, Inf),
      labels = c("18-39", "40-59", "60-79", "80+"),
      right = FALSE
    ),
    
    sex = fct_case_when(
      sex == "F" ~ "Female",
      sex == "M" ~ "Male",
      TRUE ~ NA_character_
    ),
    
    ethnicity = fct_case_when(
      ethnicity == "0" ~ "Unknown",
      ethnicity == "1" ~ "White",
      ethnicity == "2" ~ "Mixed",
      ethnicity == "3" ~ "Asian or Asian British",
      ethnicity == "4" ~ "Black or Black British",
      ethnicity == "5" ~ "Other ethnic groups",
      #TRUE ~ "Unknown"
      TRUE ~ NA_character_),
    
    bmi_group = fct_case_when(
      bmi == "Not obese" ~ "Not obese",
      bmi == "Obese I (30-34.9)" ~ "Obese I (30-34.9)",
      bmi == "Obese II (35-39.9)" ~ "Obese II (35-39.9)",
      bmi == "Obese III (40+)" ~ "Obese III (40+)",
      TRUE ~ NA_character_),

    smoking_status = fct_case_when(
      smoking_status == "S" ~ "Smoker",
      smoking_status == "E" ~ "Ever",
      smoking_status == "N" ~ "Never",
      smoking_status == "M" ~ "Missing",
      #TRUE ~ "Unknown"
      TRUE ~ NA_character_),
    
    imdQ5 = fct_case_when(
      imdQ5 == "5 (least deprived)" ~ "5 (least deprived)",
      imdQ5 == "4" ~ "4",
      imdQ5 == "3" ~ "3",
      imdQ5 == "2" ~ "2",
      imdQ5 == "1 (most deprived)" ~ "1 (most deprived)",
      TRUE ~ NA_character_
    ),
  
    region_nhs = fct_case_when(
      region_nhs == "London" ~ "London",
      region_nhs == "East" ~ "East of England",
      region_nhs == "East Midlands" ~ "East Midlands",
      region_nhs == "North East" ~ "North East",
      region_nhs == "North West" ~ "North West",
      region_nhs == "South East" ~ "South East",
      region_nhs == "South West" ~ "South West",
      region_nhs == "West Midlands" ~ "West Midlands",
      region_nhs == "Yorkshire and The Humber" ~ "Yorkshire and the Humber",
      #TRUE ~ "Unknown",
      TRUE ~ NA_character_),
    
    # Rural/urban
    rural_urban = fct_case_when(
      rural_urban %in% c(1:2) ~ "Urban - conurbation",
      rural_urban %in% c(3:4) ~ "Urban - city and town",
      rural_urban %in% c(5:6) ~ "Rural - town and fringe",
      rural_urban %in% c(7:8) ~ "Rural - village and dispersed",
      #TRUE ~ "Unknown",
      TRUE ~ NA_character_
    ),
    
    # SGTF
   sgtf = fct_case_when(
     is.na(sgtf) | sgtf == "" ~ "Unknown",
      sgtf == 1  ~ "Isolate with confirmed SGTF",
      sgtf == 0  ~ "S gene detected",
      sgtf == 9 ~ "Cannot be classified",
      #TRUE ~ "Unknown",
      TRUE ~ NA_character_
    ),
    
    # Time-between positive test and last vaccination
    tb_postest_vacc = ifelse(!is.na(date_most_recent_cov_vac),
                             difftime(date_most_recent_cov_vac, covid_test_positive_date) %>% as.numeric() %>% abs(), 
                             NA_integer_),
    
    tb_postest_vacc_cat = fct_case_when(
      is.na(tb_postest_vacc) ~ "Unknown",
      tb_postest_vacc < 7 ~ "< 7 days",
      tb_postest_vacc >=7 & tb_postest_vacc <28 ~ "7-27 days",
      tb_postest_vacc >= 28 & tb_postest_vacc <84 ~ "28-83 days",
      tb_postest_vacc >= 84 ~ ">= 84 days"
      ),
    
    # NEUTRALISING MONOCLONAL ANTIBODIES OR ANTIVIRALS ----
    # Treatment assignment window 'treated within 5 days -> <= 4 days'
    treat_window = covid_test_positive_date + days(4),
    
    # Time-between positive test and day of treatment
    tb_postest_treat = ifelse(!is.na(date_treated), 
                              difftime(date_treated, covid_test_positive_date) %>% as.numeric() %>% abs(), 
                              NA_integer_),
    
    # Flag records where treatment date falls in treatment assignment window
    treat_check = ifelse(date_treated >= covid_test_positive_date & 
                           date_treated <= treat_window, 
                         1, 
                         0),
    
    # Treatment strategy categories
    treatment_strategy_cat = case_when(
      date_treated == sotrovimab_covid_therapeutics & 
        treat_check == 1 ~ "Sotrovimab",
      date_treated == molnupiravir_covid_therapeutics & 
        treat_check == 1 ~ "Molnupiravir",
      TRUE ~ "Untreated"
    ) %>% as.factor(),
    
    # Treatment strategy overall
    treatment = case_when(
      (date_treated == sotrovimab_covid_therapeutics & 
         treat_check == 1) | 
        (date_treated == molnupiravir_covid_therapeutics & 
           treat_check == 1)  ~ "Treated",
      TRUE ~ "Untreated"
    ) %>% as.factor(),
    
    # Treatment date
    treatment_date = ifelse(treatment == "Treated", date_treated, NA_Date_),
    
    # Identify patients treated with sot and mol on same day
    treated_sot_mol_same_day = 
      case_when(is.na(sotrovimab_covid_therapeutics) ~ 0,
                is.na(molnupiravir_covid_therapeutics) ~ 0,
                sotrovimab_covid_therapeutics == 
                  molnupiravir_covid_therapeutics ~ 1,
                TRUE ~ 0),
    
    # Time-between symptom onset and treatment in those treatead
    tb_symponset_treat = 
      case_when(is.na(date_treated) ~ NA_real_,
                symptomatic_covid_test == "Y" ~ min(covid_test_positive_date,
                                                    covid_symptoms_snomed) %>%
                                                difftime(., date_treated) %>%
                                                as.numeric()
                ),
  ) %>%
  # because makes logic better readable
  rename(covid_death_date = died_ons_covid_any_date) %>%
  # add columns first admission in day 0-6, second admission etc. to be used
  # to define hospital admissions (hosp admissions for sotro treated are
  # different from the rest as sometimes their admission is just an admission
  # to get the sotro infusion)
  summarise_covid_admissions() %>%
  # adds column covid_hosp_admission_date
  add_covid_hosp_admission_outcome() %>%
  # idem as explained above for all cause hospitalisation
  summarise_allcause_admissions() %>%
  # adds column allcause_hosp_admission_date
  add_allcause_hosp_admission_outcome() %>%
  mutate(
    # Outcome prep --> outcomes are added in add_*_outcome() functions below
    study_window = covid_test_positive_date + days(27),
    # make distinction between noncovid death and covid death, since noncovid
    # death is a censoring event and covid death is an outcome
    noncovid_death_date = case_when(
      !is.na(death_date) & is.na(covid_death_date) ~ death_date,
      TRUE ~ NA_Date_
    ),
    # make distinction between noncovid hosp admission and covid hosp
    # admission, non covid hosp admission is not used as a censoring event in
    # our study, but we'd like to report how many pt were admitted to the 
    # hospital for a noncovid-y reason before one of the other events
    noncovid_hosp_admission_date = case_when(
      !is.na(allcause_hosp_admission_date) & 
        is.na(covid_hosp_admission_date) ~ allcause_hosp_admission_date,
      TRUE ~ NA_Date_
    ),
  ) %>%
  # adds column status_all and fu_all 
  add_status_and_fu_all() %>%
  # adds column status_primary and fu_primary
  add_status_and_fu_primary() %>%
  # adds column status_secondary and fu_secondary
  add_status_and_fu_secondary()

## Apply additional eligibility and exclusion criteria
data_processed_eligible <- data_processed %>%
  filter(
    # Exclude patients treated with both sotrovimab and molnupiravir on the same
    # day 
    treated_sot_mol_same_day  == 0,
  )

cat("#### data_processed ####\n")
print(dim(data_processed))

cat("#### data_processed_eligible ####\n")
print(dim(data_processed_eligible))

data_processed_eligible_day0 <- data_processed_eligible

# in the initial analysis, all patients with an outcome on day 0, 1, 2, 3, or 4,
# are excluded. [FYI, secondary outcomes are 'dereg', 'allcause_hosp' or
# 'allcause_death']
data_processed_eligible_day5 <- 
  data_processed_eligible %>%
  filter(fu_secondary > 4) %>% 
  mutate(fu_primary = fu_primary - 5, 
         fu_secondary = fu_secondary - 5) # Because starting at day 5

cat("#### data_processed_eligible day 0 ####\n")
print(dim(data_processed_eligible_day0))

cat("#### data_processed_eligible day 5 ####\n")
print(dim(data_processed_eligible_day5))

# save data
# data_processed_eligible_day0 and data_processed_eligible_day5 are saved
# and data_processed with all patients is not saved to save memory
fs::dir_create(here::here("output", "data"))
write_rds(data_processed_eligible_day0, 
          here::here("output", "data", "data_processed_day0.rds"))
write_rds(data_processed_eligible_day5, 
          here::here("output", "data", "data_processed_day5.rds"))