icpower_weibull: Study design in the presence of error-prone diagnostic tests and self-reported outcomes for Weibull model

Description

This functions works same way as icpower function except that it assumes the survival function follows Weibull distribution. The scale parameter is assumed to be same for both treatment and control groups. This can be used estimate power and sample size for interval censored data using Weibull model, which is a cpecial case when both sensitivity and specificity being 1.

Usage

icpower_weibull(
  HR,
  sensitivity,
  specificity,
  shape,
  scale,
  times,
  N = NULL,
  power = NULL,
  rho = 0.5,
  alpha = 0.05,
  pmiss = 0,
  pcensor = 0,
  design = "MCAR",
  negpred = 1
)

Arguments

hazard ratio under the alternative hypothesis.

sensitivity

the sensitivity of test.

specificity

the specificity of test

shape

shape parameter of the Weibull distribution for reference group

scale

scale parameter of the Weibull distributions. Same for all groups

times

the visit times

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.

pcensor

a value or a vector (must have same length as testtimes) of the probability of censoring at each visit, assuming censoring process is independent on other missing mechanisms.

design

missing mechanism: "MCAR" or "NTFP".

negpred

baseline negative predictive value, i.e. the probability of being truely disease free for those who were tested (reported) as disease free at baseline. If baseline screening test is perfect, then negpred = 1.

Value

result: a data frame with calculated sample size and power
I1 and I2: calculated unit Fisher information matrices for each group, which can be used to calculate more values of sample size and power for the same design without the need to enumerate again

Details

To calculate sample sizes for a vector of powers, set N = NULL. To calculate powers for a vector of sample sizes, set power = NULL. One and only one of power and N should be specified, and the other set to NULL. This function uses an enumeration algorithm to calculate the expected Fisher information matrix. The expected Fisher information matrix is used to obtain the variance of the coefficient corresponding to the treatment group indicator.

Examples

Run this code

# NOT RUN {
icpower_weibull(2, 0.75, 0.98, 1, 0.1, 1:8, power = 0.9)$result

# Interval censoring
icpower_weibull(2, 1, 1, 1, 0.1, 1:8, power = 0.9)$result

# }