Summarise relative risk

Calculate the average relative risk across individuals in a subgroup, or calculate the subgroup specific attributable fraction based on the current relative risks.

subgroupRisk(
  data,
  label = NULL,
  disease_names = c("Pharynx", "Oral_cavity"),
  af = FALSE,
  use_weights = FALSE,
  year_range = "all",
  pool = FALSE,
  subgroups = c("sex", "age_cat"),
  mort_or_morb = "mort",
  alc_mort_and_morb = c("Ischaemic_heart_disease", "LiverCirrhosis",
    "Haemorrhagic_Stroke", "Ischaemic_Stroke"),
  substance = c("alc", "tob", "tobalc")[3],
  smooth = FALSE,
  oesoph_subtypes = FALSE
)

Arguments

data

A data table of individual characteristics.

label

Character - a label to append to the outcome variable to help identify it in later calculations.

disease_names

Character vector - the names of the diseases for which summaries of relative risk are required.

af

Logical - if TRUE, then attributable fractions are calculated. If FALSE, then the total relative risk is calculated. Defaults to FALSE.

use_weights

Logical - should the calculation account for survey weights. Defaults to FALSE. Weight variable must be called "wt_int".

year_range

Either an integer vector of the years to be selected or "all". Defaults to "all".

pool

Logical - should the years selected be pooled. Defaults to FALSE.

subgroups

Character vector - the variable names of the subgroups used to stratify the estimates.

mort_or_morb

Character - for alcohol related diseases that have separate relative risk curves for mortality and morbidity, should the risks corresponding to mortality ("mort") or morbidity ("morb") be used.

alc_mort_and_morb

Character vector of the names of the alcohol related diseases that have separate risk functions for mortality and morbidity.

substance

Whether to compute relative risks for just alcohol ("alc"), just tobacco ("tob") or joint risks for tobacco and alcohol ("tobalc").

smooth

Logical - should the age patterns in average risk be smoothed with a moving average. For use only if average risk is stratified by single years of age. Defaults to FALSE

oesoph_subtypes

Logical - should the attributable fractions for oesophageal cancer be multiplied by the proportions of each subtype. Defaults to FALSE.

Value

Returns a data table containing the subgroup specific summaries for each disease.

Details

Attributable fractions are calculated using the method as in Bellis & Jones 2014, which is also equivalent to the method described in the Brennan et al. 2015 SAPM mathematical description paper.

Examples

if (FALSE) {
# Simulate individual data

# Using the parameters for the Gamma distribution from Kehoe et al. 2012
n <- 1e4
grams_ethanol_day <- rgamma(n, shape = 0.69, scale = 19.03)

data <- data.table(
  year = 2016,
  weekmean = grams_ethanol_day * 7 / 8,
  peakday = 2 * grams_ethanol_day / 8,
  age = rpois(n, 30),
  sex = sample(x = c("Male", "Female"), size = n, replace = T),
  income5cat = "1_lowest income",
  imd_quintile = "5_most_deprived",
  kids = "0",
  social_grade = "C2DE",
  eduend4cat = "16-18", # age finished education
  ethnic2cat = "white", # white / non-white
  employ2cat = "yes", # employed / not
  wtval = rnorm(n, mean = 60, sd = 5), # weight in kg
  htval = rnorm(n, mean = 1.7, sd = .1) # height in m
)

# Disease names
alc_disease_names <- c(
  "Pharynx",
  "Ischaemic_heart_disease",
  "LiverCirrhosis",
  "Transport_injuries",
  "Alcohol_poisoning",
  "Alcoholic_gastritis"
)

# Run basic function without alcohol lags
test_data <- RRFunc(
  data = copy(data),
  substance = "alc",
  alc_diseases = alc_disease_names,
  alc_wholly_chronic_thresholds = c(2, 2),
  alc_wholly_acute_thresholds = c(3, 3),
  show_progress = TRUE
)

# Calculate alcohol attributable fractions
test_aafs <- subgroupRisk(
  data = test_data$data_plus_rr,
  disease_names = alc_disease_names,
  af = TRUE,
  subgroups = "sex"
)

test_aafs
}