Determine Required Sample Size for Deming Regression — deming_sample

Automatically determines the minimum sample size needed to achieve target statistical power for detecting specified bias in method comparison studies using Deming regression.

Usage

deming_sample_size(
  target_power = 0.9,
  initial_n = 20,
  max_n = 500,
  n_sims = 500,
  use_joint = TRUE,
  step_size = 5,
  ...
)

Arguments

target_power: Desired statistical power (e.g., 0.80 or 0.90). Default is 0.90.
initial_n: Starting sample size for search. Default is 20.
max_n: Maximum sample size to try. Default is 500.
n_sims: Number of simulations per sample size tested. Default is 500.
use_joint: Logical. If TRUE, optimizes for joint region power; if FALSE, for CI power.
step_size: Step size for sample size increments. Default is 5.
...: Additional arguments passed to deming_power_sim()

Value

A list of class "deming_sample_size" containing:

n_required_ci: Required N for confidence intervals
n_required_joint: Required N for joint confidence region
target_power: Target power level
power_curve: Data frame with N and power for both methods
reduction_n: Sample size reduction using joint method
reduction_pct: Percentage reduction

Details

This function performs a grid search over sample sizes to find the minimum N needed to achieve the target power. It tests both confidence interval and joint confidence region approaches, allowing comparison of required sample sizes.

Using joint confidence regions typically requires 20-50% fewer samples than confidence intervals when the measurement range is narrow (max:min ratio < 10:1).

Examples

if (FALSE) { # \dontrun{
# Determine N needed for 90% power to detect 5% bias
sample_size_result <- deming_sample_size(
  target_power = 0.90,
  initial_n = 30,
  max_n = 200,
  n_sims = 500,
  x_range = c(20, 200),
  actual_slope = 1.05,
  ideal_slope = 1.0,
  y_var_params = list(beta1 = 1, beta2 = 0.05, J = 2, type = "power"),
  x_var_params = list(beta1 = 0.8, beta2 = 0.04, J = 2, type = "power")
)

print(sample_size_result)
plot(sample_size_result)
} # }