Description Usage Arguments Details References See Also Examples
This function creates an object of class Design
which can be added to
an object of class DataModel
.
1 2 3 4 5 6 7 8 9 |
enroll.period |
defines the length of the enrollment period. |
enroll.dist |
defines the enrollment distribution. |
enroll.dist.par |
defines the parameters of the enrollment distribution (optional). |
followup.period |
defines the length of the follow-up period for each
patient in study designs with a fixed follow-up period, i.e., the length of
time from the enrollment to planned discontinuation is constant across
patients. The user must specify either |
study.duration |
defines the total study duration in study designs with a variable follow-up period. The total study duration is defined as the length of time from the enrollment of the first patient to the discontinuation of the last patient. |
dropout.dist |
defines the dropout distribution. |
dropout.dist.par |
defines the parameters of the dropout distribution. |
Objects of class Design
are used in objects of class DataModel
to specify the design parameters used in event-driven designs if the user is
interested in modeling the enrollment (or accrual) and dropout (or loss to
follow up) processes that will be applied to the Clinical Scenario. Several
objects of class Design
can be added to an object of class
DataModel
.
Note that the length of the enrollment period, total study duration and follow-up periods are measured using the same time units.
If enroll.dist = "UniformDist"
, the enroll.dist.par
should be
let to NULL
(then enrollment distribution will be uniform during the
enrollment period).
If enroll.dist = "BetaDist"
, the enroll.dist.par
should
contain the parameter of the beta distribution (a
and b
).
These parameters must be derived according to the expected enrollment at a
specific timepoint. For example, if half the patients are expected to be
enrolled at 75% of the enrollment period, the beta distribution is a
Beta(log(0.5)/log(0.75), 1)
. Generally, let q
be the
proportion of enrolled patients at p
% of the enrollment period, the
Beta distribution can be derived as follows:
If q
>
p
, the Beta distribution is Beta(a,1)
with a = log(p) /
log(q)
If q
< p
, the Beta distribution is Beta
(1,b)
with b = log(1-p) / log(1-q)
Otherwise the Beta
distribution is Beta(1,1)
If dropout.dist = "UniformDist"
, the dropout.dist.par
should
contain the dropout rate. This parameter must be specified using the
prop
parameter, such as dropout.dist.par = parameters(prop =
0.1)
for a 10% dropout rate.
http://gpaux.github.io/Mediana/
See Also DataModel
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## Create DataModel object with a Design Object
data.model = DataModel() +
Design(enroll.period = 9,
study.duration = 21,
enroll.dist = "UniformDist",
dropout.dist = "ExpoDist",
dropout.dist.par = parameters(rate = 0.0115))
## Create DataModel object with several Design Objects
design1 = Design(enroll.period = 9,
study.duration = 21,
enroll.dist = "UniformDist",
dropout.dist = "ExpoDist",
dropout.dist.par = parameters(rate = 0.0115))
design2 = Design(enroll.period = 18,
study.duration = 24,
enroll.dist = "UniformDist",
dropout.dist = "ExpoDist",
dropout.dist.par = parameters(rate = 0.0115))
data.model = DataModel() +
design1 +
design2
|
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