ageSpline <- function(x) { 

  max((x-51)/10.289,0)^3 +  

    (69-51) * (max((x-84)/10.289,0)^3) - 

    ((84-51) * (max(((x-69))/10.289,0))^3)/(84-69) 

}

caliberScaleUnits <- function(x, quantity) {

  if(quantity == 'age') {

    ## Spline function

    x <- sapply(x, ageSpline)

  } else if(quantity == 'total_chol_6mo') {

    ## Total cholesterol, per 1 mmol/L increase

    x <- x - 5

  } else if(quantity == 'hdl_6mo') {

    ## HDL, per 0.5 mmol/L increase

    x <- (x - 1.5) / 0.5

  } else if(quantity == 'pulse_6mo') {

    ## Heart rate, per 10 b.p.m increase

    x <- (x - 70) / 10

  } else if(quantity == 'crea_6mo') {

    ## Creatinine, per 30 μmol/L increase

    x <- (x - 60) / 30

  } else if(quantity == 'total_wbc_6mo') {

    ## White cell count, per 1.5 109/L increase

    x <- (x - 7.5) / 1.5

  } else if(quantity == 'haemoglobin_6mo') {

    ## Haemoglobin, per 1.5 g/dL increase

    x <- (x - 13.5) / 1.5

  }

  # Return transformed values

  x

}

caliberScale <- function(df, surv.time, surv.event) {

  # Return a data frame with all quantities normalised/scaled/standardised to

  # ranges etc used in Rapsomaniki et al. 2014

  # imd_score is sometimes turned into a factor, which causes an error with the

  # quantile function. Since it's just integers, make it numeric.

  df$imd_score <- as.numeric(df$imd_score)

  data.frame(

    ## Time to event

    surv_time = df[, surv.time],

    ## Death/censorship

    surv_event = df[, surv.event] %in% surv.event.yes,

    ## Rescaled age

    age = sapply(df$age, ageSpline),

    ## Gender

    gender = df$gender,

    ## Most deprived quintile, yes vs. no

    most_deprived =

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

    ### SCAD diagnosis and severity ############################################

    ## Other CHD / unstable angina / NSTEMI / STEMI vs. stable angina

    diagnosis = factorChooseFirst(factor(df$diagnosis), 'SA'),

    ## PCI in last 6 months, yes vs. no

    pci_6mo = df$pci_6mo,

    ## CABG in last 6 months, yes vs. no

    cabg_6mo = df$cabg_6mo,

    ## Previous/recurrent MI, yes vs. no

    hx_mi = df$hx_mi,

    ## Use of nitrates, yes vs. no

    long_nitrate = df$long_nitrate,

    ### CVD risk factors #######################################################

    ## Ex-smoker vs. never / Current smoker vs. never

    smokstatus = factorChooseFirst(factor(df$smokstatus), 'Non'),

    ## Hypertension, present vs. absent

    hypertension = df$hypertension,

    ## Diabetes mellitus, present vs. absent

    diabetes_logical = df$diabetes != 'No diabetes',

    ## Total cholesterol, per 1 mmol/L increase

    total_chol_6mo = caliberScaleUnits(df$total_chol_6mo, 'total_chol_6mo'),

    ## HDL, per 0.5 mmol/L increase

    hdl_6mo = caliberScaleUnits(df$hdl_6mo, 'hdl_6mo'),

    ### CVD co-morbidities #####################################################

    ## Heart failure, present vs. absent

    heart_failure = df$heart_failure,

    ## Peripheral arterial disease, present vs. absent

    pad = df$pad,

    ## Atrial fibrillation, present vs. absent

    hx_af = df$hx_af,

    ## Stroke, present vs. absent

    hx_stroke = df$hx_stroke,

    ### Non-CVD comorbidities ##################################################

    ## Chronic kidney disease, present vs. absent

    hx_renal = df$hx_renal,

    ## Chronic obstructive pulmonary disease, present vs. absent

    hx_copd = df$hx_copd,

    ## Cancer, present vs. absent

    hx_cancer = df$hx_cancer,

    ## Chronic liver disease, present vs. absent

    hx_liver = df$hx_liver,

    ### Psychosocial characteristics ###########################################

    ## Depression at diagnosis, present vs. absent

    hx_depression = df$hx_depression,

    ## Anxiety at diagnosis, present vs. absent

    hx_anxiety = df$hx_anxiety,

    ### Biomarkers #############################################################

    ## Heart rate, per 10 b.p.m increase

    pulse_6mo = caliberScaleUnits(df$pulse_6mo, 'pulse_6mo'),

    ## Creatinine, per 30 μmol/L increase

    crea_6mo = caliberScaleUnits(df$crea_6mo, 'crea_6mo'),

    ## White cell count, per 1.5 109/L increase

    total_wbc_6mo = caliberScaleUnits(df$total_wbc_6mo, 'total_wbc_6mo'),

    ## Haemoglobin, per 1.5 g/dL increase

    haemoglobin_6mo = caliberScaleUnits(df$haemoglobin_6mo, 'haemoglobin_6mo')

  )

}