deming_power: Simulate Deming Regression Power

In SimplyAgree: Flexible and Robust Agreement and Reliability Analyses

Simulate Deming Regression Power

Description

Functions for conducting power analysis and sample size determination for Deming regression in method comparison studies. These functions help determine the sample size needed to detect specified biases (proportional and/or constant) between two measurement methods.

Estimates statistical power to detect deviations from the line of identity using simulated data with known properties.

Usage

deming_power_sim(
  n_sims = 1000,
  sample_size = 50,
  x_range = c(10, 100),
  x_dist = "uniform",
  actual_slope = 1.05,
  actual_intercept = 0,
  ideal_slope = 1,
  ideal_intercept = 0,
  y_var_params = list(beta1 = 1, beta2 = 0, J = 1, type = "constant"),
  x_var_params = list(beta1 = 1, beta2 = 0, J = 1, type = "constant"),
  weighted = FALSE,
  conf.level = 0.95
)

Arguments

n_sims	Number of simulation iterations. Default is 1000.
sample_size	Sample size (number of paired observations) per simulation.
x_range	Numeric vector of length 2 specifying min and max of X values (e.g., c(10, 100)).
x_dist	Character specifying distribution of X values: "uniform", "central", or "right_skewed".
actual_slope	Actual slope used to generate data (e.g., 1.05 for 5% proportional bias).
actual_intercept	Actual intercept used to generate data.
ideal_slope	Hypothesized slope to test against (typically 1 for identity line).
ideal_intercept	Hypothesized intercept to test against (typically 0 for identity line).
y_var_params	List with Y variance parameters: beta1, beta2, J, type. Type can be "constant", "proportional", or "power". For power function: sigma^2 = (beta1 + beta2*U)^J
x_var_params	List with X variance parameters (same structure as y_var_params).
weighted	Logical. Use weighted Deming regression? Default is FALSE.
conf.level	Confidence level for tests. Default is 0.95.

Details

Power Analysis for Deming Regression

This function generates simulated datasets with specified error characteristics and tests whether confidence intervals and joint confidence regions detect deviations from hypothesized values. The joint confidence region typically provides higher statistical power, especially when the X-range is narrow (high slope-intercept correlation).

The variance functions allow flexible modeling of heteroscedastic errors:

• constant: sigma^2 = beta1

• proportional: sigma^2 = beta1 * U^2 (constant CV%)

• power: sigma^2 = (beta1 + beta2*U)^J

Value

A list of class "deming_power" containing:

power_ci_slope	Power based on slope confidence interval
power_ci_intercept	Power based on intercept confidence interval
power_either_ci	Power when either CI detects difference
power_joint	Power based on joint confidence region
settings	List of simulation settings
advantage	Difference in power between joint region and CIs

References

Sadler, W.A. (2010). Joint parameter confidence regions improve the power of parametric regression in method-comparison studies. Accreditation and Quality Assurance, 15, 547-554.

Examples

## Not run: 
# Simple example: detect 5% proportional bias with constant variance
power_result <- deming_power_sim(
  n_sims = 500,
  sample_size = 50,
  x_range = c(10, 100),
  actual_slope = 1.05,
  ideal_slope = 1.0,
  y_var_params = list(beta1 = 25, beta2 = 0, J = 1, type = "constant"),
  x_var_params = list(beta1 = 20, beta2 = 0, J = 1, type = "constant")
)
print(power_result)

# More complex: heteroscedastic errors
power_result2 <- deming_power_sim(
  n_sims = 500,
  sample_size = 75,
  x_range = c(1, 100),
  actual_slope = 1.03,
  y_var_params = list(beta1 = 0.5, beta2 = 0.05, J = 2, type = "power"),
  x_var_params = list(beta1 = 0.4, beta2 = 0.04, J = 2, type = "power"),
  weighted = TRUE
)

## End(Not run)

SimplyAgree documentation built on Jan. 22, 2026, 1:08 a.m.