README.md

In ielbadisy/CEACT: Cost-Effectiveness Analysis Toolkit for Clinical Trials

CEACT Package

CEACT: Cost-Effectiveness Analysis for Clinical Trials

Overview

CEACT (Cost-Effectiveness Analysis for Clinical Trials) is an R package designed to facilitate the economic evaluation of healthcare interventions in randomized trials. It offers a suite of functions for estimating and visualizing core cost-effectiveness metrics, including:

• Incremental Cost-Effectiveness Ratios (ICER),
• Cost-effectiveness planes,
• Cost-effectiveness acceptability curves (CEAC),
• Net monetary benefit (NMB) metrics.

CEACT is built using a formula-friendly, tidyverse-inspired interface to streamline analysis workflows.

Installation

# Install from GitHub using devtools
# install.packages("devtools")
#devtools::install_github("ielbadisy/CEACT")
library(CEACT)

Key Features

• cea(): Estimate ICER and generate a descriptive cost-effectiveness summary.
• boot_icer(): Perform bootstrap-based uncertainty analysis for ICER.
• plot_ceplane(): Visualize the cost-effectiveness plane with optional quadrant breakdown.
• plot_ceac(): Plot the cost-effectiveness acceptability curve.
• compute_nmb_ceac(): Compute expected NMB and probability of cost-effectiveness across WTP values.

Example Usage

Simulate Trial Data

set.seed(123)

control <- data.frame(
  cost = rnorm(200, 500, 100),
  effect = rnorm(200, 0.4, 0.05),
  group = "control"
)

treatment <- data.frame(
  cost = rnorm(200, 550, 100),
  effect = rnorm(200, 0.3, 0.06),
  group = "treatment"
)

df <- rbind(control, treatment)

Run Cost-Effectiveness Analysis

res_cea <- CEACT::cea(cost + effect ~ group, data = df, ref = "control")
summary(res_cea)

## Cost-Effectiveness Summary
## Formula:  cost + effect ~ group 
## Reference Group:  control 
## ICER: -522.481 
## 
##       Outcome           Control         Treatment  Delta            CI p.value
## 1   Mean Cost 499.14 (sd 94.32) 553.18 (sd 96.48) 54.035 [35.28;72.79]  <0.001
## 2 Mean Effect     0.4 (sd 0.05)     0.3 (sd 0.06) -0.103 [-0.11;-0.09]  <0.001

Bootstrap the ICER

res_boot <- CEACT::boot_icer(cost + effect ~ group, data = df, ref = "control", R = 300)
summary(res_boot)

##         Metric Estimate Observed StdError   Bias                  CI
## 1   Delta Cost   52.553   54.035    8.983 -1.481     [37.634;73.698]
## 2 Delta Effect   -0.104   -0.103    0.006  0.000     [-0.114;-0.092]
## 3         ICER -508.609 -522.481   88.503 13.873 [-718.413;-366.257]

Visualize the Cost-Effectiveness Plane

CEACT::plot_ceplane(res_boot, k = 1000)

Plot the CEAC

CEACT::plot_ceac(res_boot, wtp_range = seq(0, 20000, 1000))

Compute NMB and CEAC Table

nmb_table <- CEACT::compute_nmb_ceac(cost + effect ~ 1, data = df, wtp_range = seq(0, 20000, 1000))
head(nmb_table)

##    WTP      ENMB Prob_CE
## 1    0 -526.1603  0.0000
## 2 1000 -175.7638  0.1125
## 3 2000  174.6327  0.8100
## 4 3000  525.0292  0.9750
## 5 4000  875.4256  1.0000
## 6 5000 1225.8221  1.0000

Feedback & Contributions

We welcome feedback, issues, and pull requests. Contribute via the GitHub Issues page.

TODO

• [x] Formula-based interface across all functions
• [x] Optional quadrant labels in CE plane
• [x] Improved p-value formatting
• [x] Complete function-level documentation using roxygen2
• [ ] Fix warnings and notes
• [ ] Add unit tests using testthat
• [ ] Create a PDF vignette
• [ ] Write a comprehensive tutorial or use-case article
• [ ] Submit to CRAN

ielbadisy/CEACT documentation built on June 11, 2025, 9:19 p.m.