Clinical Scenario Evaluation
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.
CSE(data, analysis, evaluation, simulation)
data |
defines a |
analysis |
defines an |
evaluation |
defines an |
simulation |
defines a |
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. |
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.
See Also DataModel
, DataStack
, AnalysisModel
, EvaluationModel
, SimParameters
.
## 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)
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