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Run Aggregate DCEA

Source: R/04_aggregate_dcea.R
run_aggregate_dcea.Rd

Implements the aggregate DCEA approach of Love-Koh et al. (2019) Value in Health. Uses disease-level healthcare utilisation patterns to distribute average health benefits from a standard CEA across socioeconomic groups. This is the method supported by NICE (2025) as a supplementary analysis for technology appraisals.

Usage

run_aggregate_dcea(
  icer,
  inc_qaly,
  inc_cost,
  population_size,
  wtp = 20000,
  disease_icd = NULL,
  subgroup_distribution = NULL,
  baseline_health = NULL,
  equity_var = "imd_quintile",
  wtp_for_equity = NULL,
  opportunity_cost_threshold = 13000,
  psa_results = NULL
)

Arguments

icer

Incremental cost-effectiveness ratio (GBP per QALY).

inc_qaly

Incremental QALYs per patient (from base-case CEA).

inc_cost

Incremental cost per patient (from base-case CEA).

population_size

Total eligible population size (integer).

wtp

Willingness-to-pay threshold in GBP/QALY (default: 20000).

disease_icd

ICD-10 code or description for HES utilisation lookup. Used to distribute benefits across IMD groups if subgroup_distribution is NULL. Example: "C34" for lung cancer.

subgroup_distribution

Optional named numeric vector (length = number of equity subgroups) giving the proportion of patients in each group. Names should match group labels in the baseline dataset. Must sum to 1. If NULL, derived from disease_icd via internal lookup.

baseline_health

Optional tibble from get_baseline_health. If NULL, uses England IMD quintile data.

equity_var

Equity stratification variable (default: "imd_quintile").

wtp_for_equity

Optional second WTP threshold for equity-weighted analysis.

opportunity_cost_threshold

Cost per QALY of care displaced by the intervention's budget impact (default: 13000, i.e., NICE's k threshold).

psa_results

Optional data frame of PSA iteration results (one row per iteration, columns inc_qaly and inc_cost).

Value

An object of class "aggregate_dcea", a named list with:

summary

Key scalar DCEA outputs.

by_group

Per-group tibble: health gain, opportunity cost, NHB.

inequality_impact

Pre/post inequality indices.

social_welfare

Social welfare results over eta.

equity_plane_data

Data frame for plot_equity_impact_plane.

metadata

Inputs and assumptions.

References

Love-Koh J, Asaria M, Cookson R, Griffin S (2019). The Social Distribution of Health: Estimating Quality-Adjusted Life Expectancy in England. Value in Health 22(5): 518-526. doi:10.1016/j.jval.2018.10.007

See also

plot_equity_impact_plane, run_full_dcea

Examples

result <- run_aggregate_dcea(
  icer            = 25000,
  inc_qaly        = 0.5,
  inc_cost        = 12500,
  population_size = 10000,
  wtp             = 20000
)
summary(result)
#> == Aggregate DCEA Result ==
#>   ICER:             £25,000 / QALY
#>   Incremental QALY: 0.5000
#>   Incremental cost: £12,500
#>   Population size:  10,000
#>   Net Health Benefit: -4615.38 QALYs
#>   SII change:         -0.0000
#>   Decision:           Trade-off: equity gain, efficiency loss
#> 
#> -- Per-group results --
#> # A tibble: 5 × 4
#>   group_label         baseline_hale post_hale   nhb
#>   <chr>                       <dbl>     <dbl> <dbl>
#> 1 Q1 (most deprived)           52.1      52.0 -923.
#> 2 Q2                           56.3      56.2 -923.
#> 3 Q3                           59.8      59.7 -923.
#> 4 Q4                           63.2      63.1 -923.
#> 5 Q5 (least deprived)          66.8      66.7 -923.
#> 
#> -- Inequality impact --
#> # A tibble: 4 × 5
#>   index           pre     post    change pct_change
#>   <chr>         <dbl>    <dbl>     <dbl>      <dbl>
#> 1 sii        18.2     18.1     -4.97e-14  -2.74e-13
#> 2 rii         0.304    0.305    4.72e- 4   1.55e- 1
#> 3 gini        0.0487   0.0488   7.55e- 5   1.55e- 1
#> 4 atkinson_1  0.00374  0.00376  1.17e- 5   3.12e- 1