ProbCurve {PracticalEquiDesign}

Plot Sample Size Curve

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
)