Calculation based on exponentials

SampleSize estimates the sample size requirement based on the censoring proportion cens_prop, the event-time distribution of each treatment arm, and the target probability of selecting the more-effective treatment target_prob. The following example considers sample size calculation for a median survival time of 6 months in the reference arm and 8 months in the treatment arm, with an estimated censoring rate of 20% and a 80% chance of selecting the more-effective treatment. Note that, because medians are provided, the calculation is based on exponential distributions.

set.seed(101)
library(PracticalEquiDesign)
n <- SampleSize(
  med1 = 6,
  med2 = 8,
  cens_prop = 0.2,
  target_prob = 0.8
)
sprintf("Sample size: %d.", n)

## [1] "Sample size: 23."

SupProb may be used to calculate the probability of selecting the superior treatment at a given sample size. For example, the probability of selecting the more-effective treatment at the sample size recommended by SampleSize is:

set.seed(101)
prob <- SupProb(
  n = n,
  med1 = 6,
  med2 = 8,
  cens_prop = 0.2
)
sprintf("Probability of selecting the more-effective treatment: %.3f.", prob)

## [1] "Probability of selecting the more-effective treatment: 0.805."

ProbCurve plots the selection probability as a function of the sample size:

set.seed(101)
q <- ProbCurve(
  med1 = 6,
  med2 = 8,
  cens_prop = 0.2,
  delta = 5,
  min_n = 10,
  max_n = 100,
  target_prob = 0.8
)
show(q)

By default, the margin of equivalence is zero. The following example increases the marginal of practical equivalence margin to 1 month. Note that increasing the margin of practical equivalence will generally increase the required sample size.

set.seed(101)
n <- SampleSize(
  med1 = 6,
  med2 = 8,
  cens_prop = 0.2,
  margin = 1,
  target_prob = 0.8
)
sprintf("Sample size: %d.", n)

## [1] "Sample size: 28."

Calculation based on Weibulls

Given the survival probabilities at two distinct time points, the function WeibullSpec determines the shape and rate parameter of the corresponding Weibull distribution. For example, suppose the anticipated survival at t1 = 6 months is 80% and that at t2 = 12 months is 50%. The corresponding Weibull distribution has shape and rate parameters:

theta1 <- WeibullSpec(t1 = 6, p1 = 0.8, t2 = 12, p2 = 0.5)
show(round(theta1, digits = 3))

## shape  rate 
## 1.635 0.067

For the comparator arm, suppose the anticipated survival at t1 = 6 months is 80%, but that the median survival is t2 = 14. The corresponding Weibull is:

theta2 <- WeibullSpec(t1 = 6, p1 = 0.8, t2 = 14, p2 = 0.5)
show(round(theta2, digits = 3))

## shape  rate 
## 1.338 0.054

To perform sample size estimation based on these hypothesized event-time distributions:

set.seed(101)
library(PracticalEquiDesign)
n <- SampleSize(
  shape1 = theta1["shape"],
  rate1 = theta1["rate"],
  shape2 = theta2["shape"],
  rate2 = theta2["rate"],
  cens_prop = 0.2,
  target_prob = 0.8
)
sprintf("Sample size: %d.", n)

## [1] "Sample size: 43."