ProbCurve: Plot Sample Size Curve

In PracticalEquiDesign: Design of Practical Equivalence Trials

Description

Plot the probability of selecting the superior treatment as a function of the sample size n.

Usage

ProbCurve(
  cens_prop = 0,
  med1 = NULL,
  shape1 = NULL,
  rate1 = NULL,
  med2 = NULL,
  shape2 = NULL,
  rate2 = NULL,
  info_reps = 50,
  delta = 1,
  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.
delta	Increment between consecutive sample sizes to evaluate.
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?

Value

ggplot object.

Examples

# Plot the selection probability curve for the case of two exponentials
# with medians of 9 and 12 (e.g.) months, and a 2 month margin of
# practical equivalence.
q <- ProbCurve(
  cens_prop = 0.15,
  med1 = 9,
  med2 = 12,
  margin = 2.0
)

PracticalEquiDesign documentation built on Dec. 6, 2021, 9:08 a.m.