scripts/04bb-calc_outcomes-GLOBAL.R

In n8thangreen/LTBIscreeningproject: LTBI screening cost-effectiveness analysis

#***************************************************************
# project: LTBI screening
# N Green
# May 2017
#
# calculate outcomes
#   - numbers of active TB cases
#   - QALYs per person for each outcome (death, avoided, cured)


n.exit_tb <-
  IMPUTED_sample_year_cohort %>%
  dplyr::filter(exituk_tb) %>%
  dplyr::count()

n.uk_tb <-
  IMPUTED_sample_year_cohort %>%
  dplyr::filter(uk_tb) %>%
  dplyr::count()


num_all_tb_cost <-
  if (interv$ENDPOINT_cost == "exit uk") {
    n.uk_tb
  } else if (interv$ENDPOINT_cost == "death") {
    n.uk_tb + n.exit_tb}

num_all_tb_QALY <-
  if (interv$ENDPOINT_QALY == "exit uk") {
    n.uk_tb
  } else if (interv$ENDPOINT_QALY == "death") {
    n.uk_tb + n.exit_tb}



# E_fatalities <- with(IMPUTED_sample_year_cohort,
#                      cfr[!is.na(cfr)])
#
# E_total_fatalities <- sum(E_fatalities)
#
# E_fatality_QALYloss <- IMPUTED_sample_year_cohort$QALY_diseasefree * E_fatalities
#
# E_total_fatality_QALYloss <- sum(E_fatality_QALYloss, na.rm = TRUE)


# adjusted_life_years type object equivalent calc
# useful for plotting...
##TODO: may need to pmax(0, .) in all_death_notif definition...
#
# QALY_diseasefree <- list()
#
# QALY_diseasefree <-
#   IMPUTED_sample_year_cohort %>%
#   subset(all_tb == TRUE) %$%
#        map2(.x = age_all_notification,
#             .y = all_death_notif,
#             .f = QALY::adjusted_life_years,
#             start_year = 0,
#             end_year = NA,
#             utility = utility$disease_free,
#             discount_rate = 0.035) %>%
#   map(QALY::total_QALYs)