SampleSize {PracticalEquiDesign}

Sample Size Estimation

Description

Estimate the sample size for a practical equivalence trial with a time to event endpoint. The sample size is determined by specifying the time to event distribution of each treatment arm, the margin of practical equivalence, and the desired probability of selecting the superior treatment. The distribution in each treatment arm may be specified either by providing the median, in which case the time to event is assumed to be exponential, or by specifying the shape and rate of a Weibull distribution. For guidance on how to set the shape and rate parameters when using a Weibull calculation, see WeibullSpec.

Usage

SampleSize(
  cens_prop = 0,
  med1 = NULL,
  shape1 = NULL,
  rate1 = NULL,
  med2 = NULL,
  shape2 = NULL,
  rate2 = NULL,
  info_reps = 50,
  min_n = 10,
  max_n = 100,
  margin = 0,
  target_prob = 0.8,
  use_exp_calc = TRUE
)

Arguments

cens_prop

Expected censoring proportion.

med1

Median for treatment arm 1, assuming shape1 is 1. Overwrites shape and rate if supplied.

shape1

Shape parameter for treatment arm 1.

rate1

Rate parameter for treatment arm 1.

med2

Median for treatment arm 2, assuming shape2 is 1. Overwrites shape and rate if supplied.

shape2

Shape parameter for treatment arm 2.

rate2

Rate parameter for treatment arm 2.

info_reps

Replicates used for estimating the observed information matrix.

min_n

Minimum allowable sample size.

max_n

Maximum allowable sample size.

margin

Margin of practical equivalence.

target_prob

Probability of selecting the more effective treatment.

use_exp_calc

If both shape parameters are 1, should the calculations be performed assuming an exponential distribution for the time to event in each arm? Default is TRUE.

Value

Integer sample size.

Examples

# Sample size calculation based on exponentials. 
n <- SampleSize(
  cens_prop = 0.15,
  med1 = 9,
  med2 = 12
)

# Sample size calculation based on exponentials with a 2 month margin.
# Note that the required sample size is expected to increase.
n <- SampleSize(
  cens_prop = 0.15,
  med1 = 9,
  med2 = 12,
  margin = 2
)

# Sample size calculation based on Weibulls. 
n <- SampleSize(
  cens_prop = 0.15,
  shape1 = 2.8,
  rate1 = 0.10,
  shape2 = 4.0,
  rate2 = 0.08
)