CSE {Mediana} | R Documentation |
Clinical Scenario Evaluation
Description
This function is used to perform the Clinical Scenario Evaluation according to the objects of class DataModel
, AnalysisModel
and EvaluationModel
specified respectively in the arguments data
, analysis
and evaluation
of the function.
Usage
CSE(data, analysis, evaluation, simulation)
Arguments
data |
defines a |
analysis |
defines an |
evaluation |
defines an |
simulation |
defines a |
Value
The CSE
function returns a list containing:
simulation.results |
a data frame containing the results of the simulations for each scenario. |
analysis.scenario.grid |
a data frame containing the grid of the combination of data and analysis scenarios. |
data.structure |
a list containing the data structure according to the |
analysis.structure |
a list containing the analysis structure according to the |
evaluation.structure |
a list containing the evaluation structure according to the |
sim.parameters |
a list containing the simulation parameters according to |
timestamp |
a list containing information about the start time, end time and duration of the simulation runs. |
References
Benda, N., Branson, M., Maurer, W., Friede, T. (2010). Aspects of modernizing drug development using clinical scenario planning and evaluation. Drug Information Journal. 44, 299-315.
http://gpaux.github.io/Mediana/
See Also
See Also DataModel
, DataStack
, AnalysisModel
, EvaluationModel
, SimParameters
.
Examples
## Not run:
# Outcome parameter set 1
outcome1.placebo = parameters(mean = 0, sd = 70)
outcome1.treatment = parameters(mean = 40, sd = 70)
# Outcome parameter set 2
outcome2.placebo = parameters(mean = 0, sd = 70)
outcome2.treatment = parameters(mean = 50, sd = 70)
# Data model
case.study1.data.model = DataModel() +
OutcomeDist(outcome.dist = "NormalDist") +
SampleSize(c(50, 55, 60, 65, 70)) +
Sample(id = "Placebo",
outcome.par = parameters(outcome1.placebo, outcome2.placebo)) +
Sample(id = "Treatment",
outcome.par = parameters(outcome1.treatment, outcome2.treatment))
# Analysis model
case.study1.analysis.model = AnalysisModel() +
Test(id = "Placebo vs treatment",
samples = samples("Placebo", "Treatment"),
method = "TTest")
# Evaluation model
case.study1.evaluation.model = EvaluationModel() +
Criterion(id = "Marginal power",
method = "MarginalPower",
tests = tests("Placebo vs treatment"),
labels = c("Placebo vs treatment"),
par = parameters(alpha = 0.025))
# Simulation Parameters
case.study1.sim.parameters = SimParameters(n.sims = 1000, proc.load = 2, seed = 42938001)
# Perform clinical scenario evaluation
case.study1.results = CSE(case.study1.data.model,
case.study1.analysis.model,
case.study1.evaluation.model,
case.study1.sim.parameters)
# Summary of the simulation results
summary(case.study1.results)
# Get the data generated for the simulation
case.study1.data.stack = DataStack(data.model = case.study1.data.model,
sim.parameters = case.study1.sim.parameters)
## End(Not run)
## Not run:
#Alternatively, a DataStack object can be used in the CSE function
# (not recommanded as the computational time is increased)
# Generate data
case.study1.data.stack = DataStack(data.model = case.study1.data.model,
sim.parameters = case.study1.sim.parameters)
# Perform clinical scenario evaluation with data stack
case.study1.results = CSE(case.study1.data.stack,
case.study1.analysis.model,
case.study1.evaluation.model,
case.study1.sim.parameters)
## End(Not run)