cea: Cost-Effectiveness Analysis Summary (Observed)

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

Cost-Effectiveness Analysis Summary (Observed)

Description

Computes observed summary statistics for a cost-effectiveness analysis comparing two groups (typically control and treatment). This includes mean and standard deviation of cost and effect, differences (deltas), confidence intervals, p-values from t-tests, and the Incremental Cost-Effectiveness Ratio (ICER).

Usage

cea(formula, data, ref, na.omit = TRUE)

Arguments

formula	A formula of the form cost + effect ~ group, where: cost is the numeric column for cost, effect is the numeric column for effectiveness or utility (e.g., QALYs), group is a grouping variable with at least two levels.
data	A data frame containing the variables used in the formula.
ref	A character string specifying the reference group in the group variable (typically "control").
na.omit	Logical; whether to remove rows with missing values. Default is TRUE.

Value

An object of class cea, which is a data frame with the following columns:

Outcome

"Mean Cost" or "Mean Effect"

Control

Mean and SD for the control group

Treatment

Mean and SD for the treatment group

Delta

Difference between treatment and control

CI

95% confidence interval for the difference

p.value

P-value from a t-test comparing groups

The object also contains attributes for the ICER, formula, reference group, and matched call.

Examples

set.seed(123)
df <- data.frame(
  cost = c(rnorm(100, 500, 100), rnorm(100, 600, 120)),
  effect = c(rnorm(100, 0.6, 0.05), rnorm(100, 0.65, 0.06)),
  group = rep(c("control", "treatment"), each = 100)
)
res <- cea(cost + effect ~ group, data = df, ref = "control")
print(res)
summary(res)

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