des_adaptive: Design an adaptive two-stage single-arm trial for a single...

In mjg211/singlearm: Design and analysis of single-arm clinical trials

Description

Determines adaptive two-stage single-arm clinical trial designs for a single binary primary endpoint.

Usage

des_adaptive(pi0 = 0.1, pi1 = 0.3, alpha = 0.05, beta = 0.2,
  Nmin = 1, Nmax = 30, optimality = "null_ess", monotonic = F,
  summary = F)

Arguments

pi0	The (undesirable) response probability used in the definition of the null hypothesis.
pi1	The (desirable) response probability used in the definition of the alternative hypothesis.
alpha	The desired maximal type-I error-rate.
beta	The desired maximal type-II error-rate.
Nmin	The minimal total sample size to allow in considered designs.
Nmax	The maximal total sample size to allow in considered designs.
optimality	Choice of optimal design criteria. Must be one of "null_ess", "alt_ess", or "minimax".
monotonic	A logical variable indicating whether the second stage sample sizes must be monotonically decreasing across their positive range.
summary	A logical variable indicating a summary of the function's progress should be printed to the console.

Details

des_adaptive() supports the determination of adaptive two-stage single-arm clinical trial designs for a single binary primary endpoint. For all supported designs, the following hypotheses are tested for the response probability π

H₀ : π = π ₀, H₁ : π = π ₁,

for π₀, π₁, satisfying π₀ < π₁, are specified using the arguments pi0 and pi1.

In each instance, the optimal design is required to meet the following operating characteristics

P(π₀) ≤ α, P(π₁) ≥ 1 - β,

where P(π) is the probability of rejecting H₀ when the true response probability is π, and the values of α and β are specified using the arguments alpha and beta respectively.

An adaptive two-stage single-arm design for a single binary endpoint, is then indexed by values for n₁, and two vectors: a₂ = (a₂₀,…,a_2n1 ) and n₂ = (n₂₀,…,n_2n1 ).

The purpose of this function is then to optimise the above parameters, accounting for the chosen restrictions placed on these vectors, and the chosen optimality criteria.

The arguments Nmin and Nmax allow restrictions to be placed on n₁ and n₂. Precisely, Nmin and Nmax set an inclusive range of allowed values for the possible minimal and maximal trial sample sizes.

In addition, monotonic also allows restrictions to be placed on n₂. Specifically, if monotonic = TRUE, the values in n₂ must be monotonically decreasing.

To describe the supported optimality criteria, denote the expected sample size and median required sample size when the true response probability is π by ESS(π) and Med(π) respectively. Then, the following optimality criteria are currently supported:

• "minimax": The design which minimises the maximal possible sample size.

• "null_ess": The design which minimises ESS(π₀).

• "alt_ess": The design which minimises ESS(π₁).

Value

A list of class "sa_des_adaptive" containing the following elements

• A list in the slot $des containing details of the identified optimal design.

• A tibble in the slot $feasible, consisting of the identified designs which met the required operating characteristics.

• Each of the input variables as specified.

See Also

opchar_adaptive, and their associated plot family of functions.

Examples

# The null-optimal design for the default parameters
null_ess <- des_adaptive()

mjg211/singlearm documentation built on May 8, 2021, 3:17 a.m.