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CEACT Package

Overview

CEACT (Cost-Effectiveness Analysis for Clinical Trials) is an R package for two-arm trial-based economic evaluation. It implements a formula-based workflow for:

CEACT is intended for individual-level clinical-trial datasets with one cost variable, one effect variable, and a two-level treatment group.

Installation

# install.packages("devtools")
devtools::install_github("ielbadisy/CEACT")
library(CEACT)

Simulate Trial Data

trial <- simulate_ce_trial(n = 200, seed = 123)
head(trial)
#>       cost    effect   group
#> 1 4495.572 0.9289862 control
#> 2 4792.840 0.8463038 control
#> 3 6402.837 0.7171661 control
#> 4 5063.458 0.7747625 control
#> 5 5116.359 0.6809741 control
#> 6 6543.558 0.6986401 control

Observed Cost-Effectiveness Summary

res_cea <- cea(cost + effect ~ group, data = trial, ref = "control")
summary(res_cea)
#> Cost-Effectiveness Summary
#> Formula: cost + effect ~ group
#> Reference group: control
#> Treatment group: treatment
#> Incremental cost: 639.489
#> Incremental effect: 0.054
#> ICER: 11818.69
#> 
#>              Outcome             Reference             Treatment Difference
#> delta_cost      Cost 4992.287 (SD 848.844) 5631.775 (SD 964.805)    639.489
#> delta_effect  Effect      0.724 (SD 0.099)      0.778 (SD 0.113)      0.054
#>                             CI p.value
#> delta_cost   [460.84; 818.138]  <0.001
#> delta_effect    [0.033; 0.075]  <0.001

Bootstrap Uncertainty

set.seed(42)
res_boot <- boot_icer(cost + effect ~ group, data = trial, ref = "control",
                      R = 500, ci.type = "perc")
summary(res_boot)
#>                   Metric  Observed BootstrapMean StdError    Bias
#> DeltaCost     Delta Cost   639.489       634.728   90.584  -4.761
#> DeltaEffect Delta Effect     0.054         0.054    0.010  -0.001
#> ICER                ICER 11818.694     12310.932 3130.320 492.238
#>                                CI
#> DeltaCost      [455.471; 810.055]
#> DeltaEffect        [0.033; 0.073]
#> ICER        [7529.884; 18944.767]

Cost-Effectiveness Plane

plot_ceplane(res_boot, k = 20000)

Net Monetary Benefit and CEAC

ceac_table <- compute_nmb_ceac(res_boot, wtp_range = seq(0, 50000, 5000))
head(ceac_table)
#>     WTP       ENMB Prob_CE
#> 1     0 -639.48888   0.000
#> 2  5000 -368.94762   0.000
#> 3 10000  -98.40636   0.224
#> 4 15000  172.13490   0.826
#> 5 20000  442.67616   0.982
#> 6 25000  713.21742   0.996
plot_ceac(ceac_table)

Deterministic Sensitivity Analysis

dsa_result <- dsa_icer(cost + effect ~ group, data = trial,
                       param = "effect",
                       range = seq(0.74, 0.82, 0.02),
                       ref = "control",
                       metric = "INMB",
                       k = 20000)
dsa_result
#>   Parameter       INMB
#> 1      0.74 -320.20684
#> 2      0.76   79.79316
#> 3      0.78  479.79316
#> 4      0.80  879.79316
#> 5      0.82 1279.79316
plot_dsa(dsa_result, metric = "INMB")

Trial-Based CEA Dataset

CEACT also includes trial_cea, a 500-patient example dataset with treatment, total cost, and QALY outcomes used in teaching material for trial-based economic evaluation.

data("trial_cea")
real_res <- cea(cost + qaly ~ group, data = trial_cea, ref = "control")
summary(real_res)
#> Cost-Effectiveness Summary
#> Formula: cost + qaly ~ group
#> Reference group: control
#> Treatment group: treatment
#> Incremental cost: 25
#> Incremental effect: 0.042
#> ICER: 588.802
#> 
#>              Outcome          Reference          Treatment Difference
#> delta_cost      Cost 3015 (SD 1582.802) 3040 (SD 1168.737)     25.000
#> delta_effect  Effect   0.573 (SD 0.217)   0.615 (SD 0.205)      0.042
#>                             CI p.value
#> delta_cost   [-219.54; 269.54]  0.8409
#> delta_effect     [0.005; 0.08]  0.0251

Package Quality

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.