Description Usage Arguments Details Value References See Also Examples
Determines single-stage single-arm clinical trial designs for a single binary primary endpoint, using either exact binomial calculations, or a normal approximation approach.
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pi0 |
The (undesirable) response probability used in the definition of the null hypothesis. Defaults to 0.1. |
pi1 |
The (desirable) response probability at which the trial is powered. Defaults to 0.3. |
alpha |
The desired maximal type-I error rate. Defaults to 0.05. |
beta |
The desired maximal type-II error rate. Defaults to 0.2. |
Nmin |
The minimal sample size to allow in considered designs. Defaults to 1. |
Nmax |
The maximal sample size to allow in considered designs. Defaults to 30. |
exact |
A logical variable indicating whether exact binomial calculations or a normal approximation approach should be used to determine the optimal design. Defaults to TRUE. |
summary |
A logical variable indicating whether a summary of the function's progress should be printed to the console. Defaults to FALSE. |
des_fixed()
supports the determination of single-stage single-arm
clinical trial designs for a single binary primary endpoint. The following
hypotheses are tested for the response probability
π
for π0, specified
using the argument pi0
.
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. Moreover,
π1, satisfying
π0 <
π1, is specified using the
argument pi1
.
A single-stage single-arm design for a single binary endpoint is ultimately indexed by three parameters: a, r, and n.
With these parameters, and denoting the number of responses after m outcomes have been observed by sm, the testing rules for the trial are as follows
If sn ≤ a, then do not reject H0.
Else if sn ≥ r, then reject H0.
The purpose of this function is then to determine (optimise) a, r, and n, accounting for the chosen restrictions placed on these parameters.
The arguments Nmin
and Nmax
allow restrictions
to be placed on n.
Precisely, Nmin
and Nmax
set an inclusive range of allowed
values for n.
Note that to ensure a decision is made about H0, this function always enforces a + 1 = r.
The optimal design is then the one that minimises n. In the case where there are multiple feasible designs with the same minimal value of n, the optimal design is the one amongst these which maximises P(π1).
If exact
is set to TRUE
then exact binomial probability
calculations are used to identify the optimal design. Otherwise, a normal
approximation approach is used. Note that setting exact = TRUE
is
recommended.
A list of class "sa_des_fixed"
containing the following
elements
A tibble in the slot $des
summarising the characteristics of the
identified optimal design. This will be NULL
if no feasible design was
identified in the considered range for
n.
A tibble in the slot $feasible
, consisting of the identified
designs which met the required operating characteristics.
Each of the input variables as specified, subject to internal modification.
A'Hern RP (2001) Sample size tables for exact single-stage phase II designs. Statistics in Medicine 20:859-66.
Fleming TR (1982) One-sample multiple testing procedure for phase II clinical trials. Biometrics 38:143-51.
opchar_fixed
, est_fixed
,
pval_fixed
, ci_fixed
, and their
associated plot
family of functions. Note that similar functionality
is available through ph2single
.
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