icensmis (version 1.4.0)

icpowerpf: Study design in the presence of interval censored outcomes (assuming perfect diagnostic tests)

Description

This function implements power and sample size calculations for interval censored time-to-event outcomes, when the diagnostic tests are assumed to be perfect (i.e. sensitivity=1 and specificity=1). This is a special case of the more general study design function icpower. However, for the special case of perfect diagnostic tests, this function can be used with significantly improved computational efficiency.

Usage

icpowerpf(
  HR,
  survivals,
  N = NULL,
  power = NULL,
  rho = 0.5,
  alpha = 0.05,
  pmiss = 0
)

Arguments

HR

hazard ratio under the alternative hypothesis.

survivals

a vector of survival function at each test time for baseline(reference) group. Its length determines the number of tests.

N

a vector of sample sizes to calculate corresponding powers. If one needs to calculate sample size, then set to NULL.

power

a vector of powers to calculate corresponding sample sizes. If one needs to calculate power, then set to NULL.

rho

proportion of subjects in baseline(reference) group.

alpha

type I error.

pmiss

a value or a vector (must have same length as survivals) of the probabilities of each test being randomly missing at each test time. If pmiss is a single value, then each test is assumed to have an identical probability of missingness.

Value

same form as returned value of icpower

Examples

# NOT RUN {
powpf1 <- icpowerpf(HR =2 , survivals = seq(0.9, 0.1, by=-0.1), N = NULL,
   power = 0.9, pmiss = 0)
   
powpf2 <- icpowerpf(HR =2 , survivals = seq(0.9, 0.1, by=-0.1), N = NULL,
   power = 0.9, pmiss = 0.7)
   
## Different missing probabilities at each test time
powpf3 <- icpowerpf(HR =2 , survivals = seq(0.9, 0.1, -0.1), N = NULL, 
   power = 0.9, pmiss = seq(0.1, .9, 0.1))    
   
# }