TOSTER (version 0.3.4)

powerTOSTtwo.prop: Power analysis for TOST for difference between two proportions using Z-test (pooled)

Description

Power analysis for TOST for difference between two proportions using Z-test (pooled)

Usage

powerTOSTtwo.prop(alpha, statistical_power, prop1, prop2, N,
  low_eqbound_prop, high_eqbound_prop)

Arguments

alpha

alpha used for the test (e.g., 0.05)

statistical_power

desired power (e.g., 0.8)

prop1

expected proportion in control condition

prop2

expected proportion in the experimental condition

N

sample size (e.g., 108)

low_eqbound_prop

lower equivalence bounds (e.g., -0.05) expressed in proportion

high_eqbound_prop

upper equivalence bounds (e.g., 0.05) expressed in proportion

Value

Calculate either achieved power, equivalence bounds, or required N, assuming a true effect size of 0. Returns a string summarizing the power analysis, and a numeric variable for number of observations, equivalence bounds, or power.

References

Silva, G. T. da, Logan, B. R., & Klein, J. P. (2008). Methods for Equivalence and Noninferiority Testing. Biology of Blood and Marrow Transplantation: Journal of the American Society for Blood and Marrow Transplantation, 15(1 Suppl), 120-127. https://doi.org/10.1016/j.bbmt.2008.10.004 Julious, S. A. & Campell, M. J. (2012). Tutorial in biostatistics: sample sizes for parallel group clinical trials with binary data. Statistics in Medicine, 31:2904-2936. Chow, S.-C., Wang, H., & Shao, J. (2007). Sample Size Calculations in Clinical Research, Second Edition (2 edition). Boca Raton: Chapman and Hall/CRC.

Examples

# NOT RUN {
## Sample size for alpha = 0.05, 90% power, assuming true effect prop1 = prop 2 = 0.5,
## equivalence bounds of 0.4 and 0.6 (so low_eqbound_prop = -0.1 and high_eqbound_prop = 0.1)

powerTOSTtwo.prop(alpha = 0.05, statistical_power = 0.9, prop1 = 0.5, prop2 = 0.5,
   low_eqbound_prop = -0.1, high_eqbound_prop = 0.1)

## Power for alpha = 0.05, N 542 , assuming true effect prop1 = prop 2 = 0.5,
## equivalence bounds of 0.4 and 0.6 (so low_eqbound_prop = -0.1 and high_eqbound_prop = 0.1)

powerTOSTtwo.prop(alpha = 0.05, N = 542, prop1 = 0.5, prop2 = 0.5,
   low_eqbound_prop = -0.1, high_eqbound_prop = 0.1)

## Equivalence bounds for alpha = 0.05, N 542 , assuming true effect prop1 = prop 2 = 0.5,
## and 90% power

powerTOSTtwo.prop(alpha=0.05, statistical_power=0.9, N=542, prop1 = 0.5, prop2 = 0.5)

#Example 4.2.4 from Chow, Wang, & Shao (2007, p. 93)
powerTOSTtwo.prop(alpha=0.05, statistical_power=0.8, prop1 = 0.75, prop2 = 0.8,
   low_eqbound_prop = -0.2, high_eqbound_prop = 0.2)

# Example 5 from Julious & Campbell (2012, p. 2932)
powerTOSTtwo.prop(alpha=0.025, statistical_power=0.9, prop1 = 0.8, prop2 = 0.8,
   low_eqbound_prop=-0.1, high_eqbound_prop=0.1)
# From Machin, D. (Ed.). (2008). Sample size tables for clinical studies (3rd ed).

# Example 9.4b equivalence of two proportions (p. 113) #
powerTOSTtwo.prop(alpha=0.010, statistical_power=0.8, prop1 = 0.5, prop2 = 0.5,
   low_eqbound_prop = -0.2, high_eqbound_prop = 0.2)/2
# }