# 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

1 2 |

### 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

1 2 3 4 5 6 7 8 9 | ```
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))
``` |