Description Usage Arguments Details Value See Also Examples
Determines adaptive two-stage single-arm clinical trial designs for a single binary primary endpoint.
1 2 3 | 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)
|
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
|
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. |
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
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
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,…,a2n1 ) and n2 = (n20,…,n2n1 ).
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).
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.
opchar_adaptive
, and their associated plot
family of functions.
1 2 | # The null-optimal design for the default parameters
null_ess <- des_adaptive()
|
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