icpowerpf {icensmis} | R Documentation |
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.
icpowerpf(HR, survivals, N = NULL, power = NULL, rho = 0.5, alpha = 0.05, pmiss = 0)
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. |
same form as returned value of icpower
See icpower
for more details in a general situation.
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))