SimplyAgree (version 0.3.0)

deming_power: 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
)

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

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

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

if (FALSE) {
# 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
)
}