etsi (version 1.0)

etsi.design: Calculates the estimated power for a future Study B or the required sample size to achieve a desired level of power.

Description

Function to help design a future Study B given data from Study A. Depending on the inputs, either calculates the estimated power in Study B, or calculates the required sample size in each treatment groupto achieve a desired level of power in Study B.

Usage

etsi.design(Study.A, n.b0 = NULL, n.b1 = NULL, psi = NULL, w.range = NULL, 
            kappa = NULL, desired.power = NULL, iterations = 100)

Value

A list is returned:

power

The estimated power in Study B; only given if desired.power = NULL.

sample.size

The required sample size in each treatment group in Study B; only given if desired.power is not NULL.

Arguments

Study.A

Study.A, a dataframe representing Study A containing the required columns A (treatment assignment), Y (primary outcome), S (surrogate outcome), and W (baseline covariate of interest). Optionally contains a column delta, which is an indicator for strong surrogacy.

n.b0

n.b0, sample size in Study B control group

n.b1

n.b1, sample size in Study B treatment group

psi

psi, the overall treatment effect you want to detect in Study B

w.range

w.range, a vector containing the lower and upper limits of the covariate W you plan to have in Study B

kappa

kappa, the threshold to determine strong surrogacy when the delta columns is not provided in Study.A. Surrogacy is considered sufficiently strong when the estimated PTE with respect to W is greater than kappa.

desired.power

The desired power in Study B, when you want to calculate required sample size

iterations

The number of iterations used for generalized cross-validation.

Author

Rebecca Knowlton

References

Knowlton, R., Parast, L. (2025) "Efficient Testing Using Surrogate Information." Biometrical Journal, 67(6): e70086.

Examples

  data(exampledataA)
  data(exampledataB)
  names(exampledataA)
  names(exampledataB)
  etsi.design(Study.A = exampledataA,
            n.b0 = 400, 
            n.b1 = 500, 
            iterations = 10)