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

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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

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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

Note

See icpower for more details in a general situation.

Examples

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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))