This function calculates the power for the Bland-Altman method under varying parameter settings and for a range of sample sizes.

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

## Arguments

samplesizes

vector of samples sizes at which to estimate power.

mu

mean of differences

SD

standard deviation of differences

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.

## Value

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

## 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), https://doi.org/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 × 5
#>   delta conf.level agree.level power     N
#>   <dbl>      <dbl>       <dbl> <dbl> <dbl>
#> 1     8       0.95        0.95 0.800   145
# }