powerTOSTpaired {TOSTER}

Power Paired Sample t-test

Description

Power analysis for TOST for dependent t-test (Cohen's dz). This function is no longer maintained please use power_t_TOST.

Usage

powerTOSTpaired(alpha, statistical_power, N, low_eqbound_dz, high_eqbound_dz)

powerTOSTpaired.raw(
  alpha,
  statistical_power,
  low_eqbound,
  high_eqbound,
  sdif,
  N
)

Arguments

alpha

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

statistical_power

desired power (e.g., 0.8)

N

number of pairs (e.g., 96)

low_eqbound_dz

lower equivalence bounds (e.g., -0.5) expressed in standardized mean difference (Cohen's dz)

high_eqbound_dz

upper equivalence bounds (e.g., 0.5) expressed in standardized mean difference (Cohen's dz)

low_eqbound

lower equivalence bounds (e.g., -0.5) expressed in raw mean difference

high_eqbound

upper equivalence bounds (e.g., 0.5) expressed in raw mean difference

sdif

standard deviation of the difference scores

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

Chow, S.-C., Wang, H., & Shao, J. (2007). Sample Size Calculations in Clinical Research, Second Edition - CRC Press Book. Formula 3.1.9

Examples

## Sample size for alpha = 0.05, 80% power, equivalence bounds of
## Cohen's dz = -0.3 and Cohen's d = 0.3, and assuming true effect = 0
powerTOSTpaired(alpha=0.05,statistical_power=0.8,low_eqbound_dz=-0.3,high_eqbound_dz=0.3)

## Sample size for alpha = 0.05, N = 96 pairs, equivalence bounds of
## Cohen's dz = -0.3 and Cohen's d = 0.3, and assuming true effect = 0
powerTOSTpaired(alpha=0.05,N=96,low_eqbound_dz=-0.3,high_eqbound_dz=0.3)

## Equivalence bounds for alpha = 0.05, N = 96 pairs, statistical power of
## 0.8, and assuming true effect = 0
powerTOSTpaired(alpha=0.05,N=96,statistical_power=0.8)

## Sample size for alpha = 0.05, 80% power, equivalence bounds of -3 and 3 in raw units
## and assuming a standard deviation of the difference scores of 10, and assuming a true effect = 0
powerTOSTpaired.raw(alpha=0.05,statistical_power=0.8,low_eqbound=-3, high_eqbound=3, sdif=10)

## Sample size for alpha = 0.05, N = 96 pairs, equivalence bounds of -3 and 3 in raw units
## and assuming a standard deviation of the difference scores of 10, and assuming a true effect = 0
powerTOSTpaired.raw(alpha=0.05,N=96,low_eqbound=-3, high_eqbound=3, sdif=10)

## Equivalence bounds for alpha = 0.05, N = 96 pairs, statistical power of 0.8
## and assuming a standard deviation of the difference scores of 10, and assuming a true effect = 0