The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Full-Form DCEA Tutorial

When to use full-form DCEA

Full-form DCEA is appropriate when subgroup-specific clinical evidence is available — for example, differential uptake by deprivation, differential survival benefit by SES, or trial subgroup data stratified by socioeconomic characteristics.

The inequality staircase

The five steps of the staircase define how patient-level benefit distributes across IMD groups:

  1. Disease prevalence — who gets the disease?
  2. Eligibility — who meets criteria for the treatment?
  3. Uptake / access — who actually receives it?
  4. Clinical effect — what QALY gain does each group achieve?
  5. Opportunity cost — what health is displaced in each group?

Worked example: NSCLC with subgroup evidence

baseline <- get_baseline_health("england", "imd_quintile")

Subgroup-specific CEA inputs

subgroup_data <- tibble::tibble(
  group       = 1:5,
  group_label = paste("IMD Q", 1:5),
  inc_qaly    = c(0.28, 0.36, 0.44, 0.51, 0.57),
  inc_cost    = c(13200, 12800, 12400, 12000, 11600),
  pop_share   = c(0.28, 0.24, 0.20, 0.16, 0.12)
)
subgroup_data
#> # A tibble: 5 × 5
#>   group group_label inc_qaly inc_cost pop_share
#>   <int> <chr>          <dbl>    <dbl>     <dbl>
#> 1     1 IMD Q 1         0.28    13200      0.28
#> 2     2 IMD Q 2         0.36    12800      0.24
#> 3     3 IMD Q 3         0.44    12400      0.2 
#> 4     4 IMD Q 4         0.51    12000      0.16
#> 5     5 IMD Q 5         0.57    11600      0.12

Differential uptake

uptake <- c(0.58, 0.63, 0.68, 0.73, 0.77)

Run full-form DCEA

result_full <- run_full_dcea(
  subgroup_cea_results       = subgroup_data,
  baseline_health            = baseline,
  wtp                        = 20000,
  opportunity_cost_threshold = 13000,
  uptake_by_group            = uptake
)
summary(result_full)
#> == Full-Form DCEA Result ==
#>   Net Health Benefit (equity-weighted): -0.1400 QALYs
#>   SII change: 0.2080
#>   Decision:   Lose-Lose (efficiency loss + equity loss)
#> 
#> -- Per-group results --
#> # A tibble: 5 × 5
#>   group_label inc_qaly_adj      nhb baseline_hale post_hale
#>   <chr>              <dbl>    <dbl>         <dbl>     <dbl>
#> 1 IMD Q 1            0.162 -0.220            52.1      51.7
#> 2 IMD Q 2            0.227 -0.176            56.3      55.9
#> 3 IMD Q 3            0.299 -0.122            59.8      59.5
#> 4 IMD Q 4            0.372 -0.0657           63.2      62.9
#> 5 IMD Q 5            0.439 -0.00770          66.8      66.6

Visualise

plot_equity_impact_plane(result_full)

Inequality staircase

sc_data <- build_staircase_data(
  group           = 1:5,
  group_labels    = paste("IMD Q", 1:5),
  prevalence      = c(0.08, 0.07, 0.06, 0.05, 0.04),
  eligibility     = c(0.70, 0.72, 0.74, 0.76, 0.78),
  uptake          = uptake,
  clinical_effect = subgroup_data$inc_qaly,
  opportunity_cost = subgroup_data$inc_cost / 13000
)
plot_inequality_staircase(sc_data)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.