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Nonparametric Test for Limits of Agreement

Source: R/agree_np.R
agree_np.Rd

[Stable]

agree_np A non-parametric approach to limits of agreement. The hypothesis test is based on binomial proportions within the maximal allowable differences, and the limits are calculated with quantile regression.

Usage

agree_np(
  x,
  y,
  id = NULL,
  data,
  delta = NULL,
  prop_bias = FALSE,
  TOST = TRUE,
  agree.level = 0.95,
  conf.level = 0.95
)

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 with samples are taken in replicates.

data

Data frame with all data.

delta

The threshold below which methods agree/can be considered equivalent and this argument is required. Equivalence Bound for Agreement or Maximal Allowable Difference.

prop_bias

Logical indicator (TRUE/FALSE) of whether proportional bias should be considered for the limits of agreement calculations.

TOST

Logical indicator (TRUE/FALSE) of whether to use two one-tailed tests for the limits of agreement. Default is TRUE.

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.

conf.level

the confidence level required. Default is 95%.

Value

Returns simple_agree object with the results of the agreement analysis.

  • loa: A data frame of the limits of agreement.

  • agree: A data frame of the binomial proportion of results in agreement.

  • h0_test: Decision from hypothesis test.

  • qr_mod: The quantile regression model.

  • call: The matched call

References

Bland, J. M., & Altman, D. G. (1999). Measuring agreement in method comparison studies. In Statistical Methods in Medical Research (Vol. 8, Issue 2, pp. 135–160). SAGE Publications. doi:10.1177/096228029900800204

Examples

data('reps')
agree_np(x = "x", y = "y", id = "id", data = reps, delta = 2)
#> Warning: Model has 4 prior weights, but we recovered 2 rows of data.
#> So prior weights were ignored.
#> Warning: Evidence of proportional bias. Consider setting prop_bias to TRUE.
#> Limit of Agreement = 95%
#> Binomial proportions test and quantile regression for LoA
#> 
#>            agreement lower.ci upper.ci
#> % within 2    0.8333   0.5914   0.9453
#> Hypothesis Test: don't reject h0
#> 
#> ###- Quantile Limits of Agreement (LoA) -###
#>           Estimate Lower CI Upper CI CI Level
#> Lower LoA    -1.12  -1.4927  -0.7473     0.90
#> Bias          0.04  -0.5694   0.6494     0.95
#> Upper LoA     2.97   2.2967   3.6433     0.90
#>