powerTOSTtwo {TOSTER}

Power Two Sample t-test

Description

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 This function is no longer maintained please use power_t_TOST.

Usage

powerTOSTtwo(alpha, statistical_power, N, low_eqbound_d, high_eqbound_d)

powerTOSTtwo.raw(
  alpha,
  statistical_power,
  N,
  sdpooled,
  low_eqbound,
  high_eqbound,
  delta = 0
)

Arguments

alpha

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

statistical_power

desired power (e.g., 0.8)

N

sample size per group (e.g., 108)

low_eqbound_d

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

high_eqbound_d

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

sdpooled

specify the pooled standard deviation

low_eqbound

lower equivalence bounds (e.g., -0.5) expressed in raw scale units (e.g., scalepoints)

high_eqbound

upper equivalence bounds (e.g., 0.5) expressed in raw scale units (e.g., scalepoints)

delta

hypothesized true value for the difference between the 2 means. Default is zero.

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 3.2.4 with k = 1

Examples

## Sample size for alpha = 0.05, 80% power, equivalence bounds of
## Cohen's d = -0.4 and Cohen's d = 0.4, assuming true effect = 0
powerTOSTtwo(alpha=0.05, statistical_power=0.8, low_eqbound_d=-0.4, high_eqbound_d=0.4)

## Statistical power for alpha = 0.05, N = 108 per group, equivalence bounds of
## Cohen's d = -0.4 and Cohen's d = 0.4, assuming true effect = 0
powerTOSTtwo(alpha=0.05, N=108, low_eqbound_d=-0.4, high_eqbound_d=0.4)

## Equivalence bounds for alpha = 0.05, N = 108 per group, statistical power of
## 0.8, assuming true effect = 0
powerTOSTtwo(alpha=0.05, N=108, statistical_power=0.8)

## Sample size for alpha = 0.05, 80% power, equivalence bounds of -200 and 200 in raw
## units, assuming pooled standard deviation of 350, and assuming true effect = 0
powerTOSTtwo.raw(alpha=0.05,statistical_power=0.8,low_eqbound=-200,high_eqbound=200,sdpooled=350)

## Power for alpha = 0.05, N = 53 per group, equivalence bounds of
## -200 and 200 in raw units, assuming sdpooled = 350 and true effect = 0
powerTOSTtwo.raw(alpha=0.05, N=53, low_eqbound=-200, high_eqbound=200, sdpooled=350)

## Equivalence bounds for alpha = 0.05, N = 108 per group, statistical power of
## 0.8, assuming true effect = 0
powerTOSTtwo.raw(alpha=0.05, N=53, statistical_power=0.8, sdpooled=350)