#' # prep-data.R

#' We start by preparing the data for reproducible comparisons...

# Set the random seed for reproducibility

random.seed <- 35498L

# Specify the data file containing the patient cohort

data.filename <- '../../data/cohort-sanitised.csv'

# Specify file to write performance characteristics to

performance.file <- '../../output/models-performance.tsv'

# The fraction of the data to use as the test set (1 - this will be used as the

# training set)

test.fraction <- 1/3

# If surv.predict wasn't already specified, use the defaults...

if(!exists('surv.predict')) {

  # Column names of variables to use for predictions

  surv.predict <- c(

    'age', 'gender', 'diagnosis', 'pci_6mo', 'cabg_6mo',

    'hx_mi', 'long_nitrate', 'smokstatus', 'hypertension', 'diabetes',

    'total_chol_6mo', 'hdl_6mo', 'heart_failure', 'pad', 'hx_af', 'hx_stroke',

    'hx_renal', 'hx_copd', 'hx_cancer', 'hx_liver', 'hx_depression',

    'hx_anxiety', 'pulse_6mo', 'crea_6mo', 'total_wbc_6mo','haemoglobin_6mo',

    'most_deprived'

  )

}

cols.keep <- c(surv.predict, 'exclude', 'imd_score')

exclude.vars <- c('hx_mi')

surv.predict <- surv.predict[!(surv.predict %in% exclude.vars)]

# Check to see if endpoint exists to avoid error

if(!exists('endpoint')) {

  # Default is all-cause mortality

  endpoint <- 'death'

}

# If we're looking at MI...

if(endpoint == 'mi') {

  surv.time      <- 'time_coronary'

  surv.event     <- 'endpoint_coronary'

  surv.event.yes <- c('Nonfatal MI', 'Coronary death')

# If dealing with death in an imputed dataset...

} else if(endpoint == 'death.imputed') {

  surv.time    <- 'time_death'

  surv.event   <- 'endpoint_death' 

  surv.event.yes <- 1 # Coded as 1s and 0s for imputation

# Default is all-cause mortality...

} else {

  # Name of column giving time for use in survival object

  surv.time    <- 'time_death'

  # Name of event column for survival object

  surv.event   <- 'endpoint_death' # Cannot be 'surv_event' or will break later!

  # Value of surv.event column if an event is recorded

  surv.event.yes <- 'Death'

}

# Quantile boundaries for discretisation

discretise.quantiles <- c(0, 0.1, 0.25, 0.5, 0.75, 0.9, 1)

# Columns to discretise in specific ways, or not discretise at all. Those not

# listed here will be discretised by quantile with the default quantiles listed

# above.

discretise.settings <-

  list(

    var        = c('anonpatid', 'surv_time', 'imd_score', 'exclude'),

    method     = c(NA, NA, NA, NA),

    settings   = list(NA, NA, NA, NA)

  )

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

### END USER VARIABLES #########################################################

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

if(!is.na(random.seed)) {

  set.seed(random.seed)

}

source('../lib/handymedical.R', chdir = TRUE)

# Define a function of extra non-general prep to be done on this dataset

caliberExclude <- function(df) {

  df <-

    df[

      # remove negative times to death

      df$surv_time > 0 &

        # remove patients who should be excluded

        !df$exclude

      ,

      ]

  # Remove the exclude column, which we don't need any more

  df$exclude <- NULL

  df

}

caliberExtraPrep <- function(df) {

  df <- caliberExclude(df)

  # Create most_deprived, as defined in the paper: the bottom 20%

  df$most_deprived <- df$imd_score > quantile(df$imd_score, 0.8, na.rm = TRUE)

  df$most_deprived <- factorNAfix(factor(df$most_deprived), NAval = 'missing')

  # Remove the imd_score, to avoid confusion later

  df$imd_score <- NULL

  df

}