ba_analysis: Bland-Altman Analysis for Method Comparison

In valytics: Statistical Methods for Analytical Method Comparison and Validation

Bland-Altman Analysis for Method Comparison

Description

Performs Bland-Altman analysis to assess agreement between two measurement methods. Calculates bias (mean difference), limits of agreement, and confidence intervals following the approach of Bland & Altman (1986, 1999).

Usage

ba_analysis(
  x,
  y = NULL,
  data = NULL,
  conf_level = 0.95,
  type = c("absolute", "percent"),
  na_action = c("omit", "fail")
)

Arguments

x	Numeric vector of measurements from method 1 (reference method), or a formula of the form method1 ~ method2.
y	Numeric vector of measurements from method 2 (test method). Ignored if x is a formula.
data	Optional data frame containing the variables specified in x and y (or in the formula).
conf_level	Confidence level for intervals (default: 0.95).
type	Type of difference calculation: "absolute" (default) for y - x, or "percent" for 100 * (y - x) / mean.
na_action	How to handle missing values: "omit" (default) removes pairs with any NA, "fail" stops with an error.

Details

The Bland-Altman method assesses agreement between two quantitative measurements by analyzing the differences against the averages. The key outputs are:

• Bias: The mean difference between methods, indicating systematic difference. A bias significantly different from zero suggests one method consistently measures higher or lower than the other.

• Limits of Agreement (LoA): The interval within which 95\ differences are expected to lie (bias +/- 1.96 x SD). These define the range of disagreement between methods.

• Confidence Intervals: CIs for bias and LoA quantify the uncertainty in these estimates due to sampling variability.

The confidence intervals for limits of agreement are calculated using the exact method from Bland & Altman (1999), which accounts for the uncertainty in both the mean and standard deviation.

Value

An object of class c("ba_analysis", "valytics_comparison", "valytics_result"), which is a list containing:

input

List with original data and metadata:

• x, y: Numeric vectors (after NA handling)

• n: Number of paired observations

• n_excluded: Number of pairs excluded due to NAs

• var_names: Named character vector with variable names

results

List with statistical results:

• differences: Numeric vector of differences (y - x or percent)

• averages: Numeric vector of means ((x + y) / 2)

• bias: Mean difference (point estimate)

• bias_se: Standard error of the bias

• bias_ci: Named numeric vector with lower and upper CI for bias

• sd_diff: Standard deviation of differences

• loa_lower: Lower limit of agreement (bias - 1.96 * SD)

• loa_upper: Upper limit of agreement (bias + 1.96 * SD)

• loa_lower_ci: Named numeric vector with CI for lower LoA

• loa_upper_ci: Named numeric vector with CI for upper LoA

settings

List with analysis settings:

• conf_level: Confidence level used

• type: Type of difference calculation

• multiplier: Multiplier for LoA (default: 1.96 for 95\

call

The matched function call.

Assumptions

The standard Bland-Altman analysis assumes:

• Differences are approximately normally distributed

• No proportional bias (constant bias across the measurement range)

• No repeated measurements per subject

References

Bland JM, Altman DG (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1(8476):307-310. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0140-6736(86)90837-8")}

Bland JM, Altman DG (1999). Measuring agreement in method comparison studies. Statistical Methods in Medical Research, 8(2):135-160. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/096228029900800204")}

See Also

plot.ba_analysis() for visualization, summary.ba_analysis() for detailed summary

Examples

# Simulated method comparison data
set.seed(42)
method_a <- rnorm(50, mean = 100, sd = 15)
method_b <- method_a + rnorm(50, mean = 2, sd = 5)  # Method B has +2 bias

# Basic analysis
ba <- ba_analysis(method_a, method_b)
ba

# Using formula interface with data frame
df <- data.frame(reference = method_a, test = method_b)
ba <- ba_analysis(reference ~ test, data = df)

# Percentage differences
ba_pct <- ba_analysis(method_a, method_b, type = "percent")

valytics documentation built on Feb. 19, 2026, 5:06 p.m.