Power Curve for Bland-Altman Limits of Agreement

This function calculates the power for the Bland-Altman method under varying parameter settings and for a range of sample sizes. It is superseded by power_agreement_limit(), which returns a power.htest object and can solve for the sample size, power, delta or the SD directly. This function remains available for drawing power curves.

Usage

blandPowerCurve(
  samplesizes = seq(10, 100, 1),
  mu = 0,
  SD,
  delta,
  conf.level = 0.95,
  agree.level = 0.95,
  m = 1,
  icc = 0
)

Arguments

samplesizes

vector of samples sizes at which to estimate power: the number of pairs, or the number of subjects when m > 1.

mu

mean of differences

SD

standard deviation of differences (the total SD when m > 1)

delta

The threshold below which methods agree/can be considered equivalent, can be in any units. Equivalence Bound for Agreement. More than one delta can be provided.

conf.level

the confidence level(s) required. Default is 95%. More than one confidence level can be provided.

agree.level

the agreement level(s) required. Default is 95%. The proportion of data that should lie between the thresholds, for 95% limits of agreement this should be 0.95. More than one confidence level can be provided.

m

number of paired measurements per subject (default 1, independent pairs).

icc

intraclass correlation of the paired differences, in [0, 1). Ignored when m = 1.

Value

A dataframe is returned containing the power analysis results. The results can then be plotted with the plot.powerCurve function.

Details

Power follows the approximate method of Lu et al. (2016). With clustered data (m > 1), the standard error of the limits uses the effective sample size n \* m / (1 + (m - 1) \* icc) for the mean difference and a Satterthwaite df for the SD, under a balanced compound symmetry model (see agree_expected_half() for the model). Like the original method, this is an approximation; m = 1 gives the original results.

References

Lu, M. J., et al. (2016). Sample Size for Assessing Agreement between Two Methods of Measurement by Bland-Altman Method. The international journal of biostatistics, 12(2), doi:10.1515/ijb-2015-0039

Examples

# \donttest{
powerCurve <- blandPowerCurve(samplesizes = seq(10, 200, 1),
mu = 0,
SD = 3.3,
delta = 8,
conf.level = .95,
agree.level = .95)
# Plot the power curve
plot(powerCurve, type = 1)

# Find at what N power of .8 is achieved
find_n(powerCurve, power = .8)
#> # A tibble: 1 × 7
#>   delta conf.level agree.level     m   icc power     N
#>   <dbl>      <dbl>       <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     8       0.95        0.95     1     0 0.800   145
# }
# If the desired power is not found then
## Sample size range must be expanded