Limits of Agreement — agreement

A function for calculating for Bland-Altman limits of agreement based on the difference between two measurements (difference = x-y). Please note that the package developer recommends reporting/using tolerance limits (see "tolerance_limit" function).

agreement_limit(
  x,
  y,
  id = NULL,
  data,
  data_type = c("simple", "nest", "reps"),
  loa_calc = c("mover", "blandaltman"),
  agree.level = 0.95,
  alpha = 0.05,
  prop_bias = FALSE,
  log_tf = FALSE
)

Arguments

x: Name of column with first measurement
y: Name of other column with the other measurement to compare to the first.
id: Column with subject identifier. Default is "id" if no entry is provided.
data: Data frame with all data.
data_type: The type of data structure. Options include "simple" (all independent data points), "nest" (nested data) and "reps" (replicated data points).
loa_calc: The method by which the limits of agreement confidence intervals are calculated. Options are "mover" (Methods of Recovering Variances method) or "blandlatman" (Bland-Altman method).
agree.level: the agreement level required. Default is 95%. The proportion of data that should lie between the thresholds, for 95% limits of agreement this should be 0.95.
alpha: The alpha-level for confidence levels.
prop_bias: Logical indicator (TRUE/FALSE) of whether proportional bias should be considered for the limits of agreement calculations.
log_tf: Calculate limits of agreement using log-transformed data.

Value

Returns single loa class object with the results of the agreement analysis.

loa: A data frame containing the Limits of Agreement.
call:The matched call.

Details

The limits of agreement (LoA) are calculated in this function are based on the method originally detailed by Bland & Atlman (1986 & 1999). The loa_calc allow users to specify the calculative method for the LoA which can be based on Bland-Altman (1999) (loa_calc = "blandaltman"), or by the more accurate MOVER method of Zou (2013) and Donner & Zou (2012) (loa_calc = "mover").

References

MOVER methods:

Zou, G. Y. (2013). Confidence interval estimation for the Bland–Altman limits of agreement with multiple observations per individual. Statistical methods in medical research, 22(6), 630-642.

Donner, A., & Zou, G. Y. (2012). Closed-form confidence intervals for functions of the normal mean and standard deviation. Statistical Methods in Medical Research, 21(4), 347-359.

Bland & Altman methods:

Bland, J. M., & Altman, D. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet, 327(8476), 307-310.

Bland, J. M., & Altman, D. (1999). Measuring agreement in method comparison studies. Statistical methods in medical research, 8(2), 135-160.

Bland, J. M., & Altman, D. G. (1996). Statistics notes: measurement error proportional to the mean. BMJ, 313(7049), 106.

Examples

data('reps')

# Simple
agreement_limit(x = "x", y ="y", data = reps)
#> MOVER Limits of Agreement (LoA)
#> 95% LoA @ 5% Alpha-Level
#> Independent Data Points
#> 
#>    Bias           Bias CI Lower LoA Upper LoA            LoA CI
#>  0.4383 [-0.1669, 1.0436]    -1.947     2.824 [-3.0117, 3.8884]
#> 
#> SD of Differences = 1.217

# Replicates
agreement_limit(x = "x", y ="y", data = reps, id = "id", data_type = "rep")
#> MOVER Limits of Agreement (LoA)
#> 95% LoA @ 5% Alpha-Level
#> Data with Replicates
#> 
#>    Bias           Bias CI Lower LoA Upper LoA            LoA CI
#>  0.7152 [-1.5287, 2.9591]    -2.232     3.662 [-7.5482, 8.9786]
#> 
#> SD of Differences = 1.5036

# Nested
agreement_limit(x = "x", y ="y", data = reps, id = "id", data_type = "nest")
#> MOVER Limits of Agreement (LoA)
#> 95% LoA @ 5% Alpha-Level
#> Nested Data
#> 
#>    Bias           Bias CI Lower LoA Upper LoA            LoA CI
#>  0.7046 [-1.5572, 2.9664]    -2.153     3.562 [-7.4979, 8.9071]
#> 
#> SD of Differences = 1.4581