Clinical Scenario Evaluation

Summary

Clinical Scenario Evaluation (CSE) is performed based on the data, analysis and evaluation models as well as simulation parameters specified by the user. The simulation parameters are defined using the SimParameters object.

Clinical Scenario Evaluation

SimParameters object

Description

The SimParameters object is a required argument of the CSE function and has the following arguments:

  • n.sims defines the number of simulations.
  • seed defines the seed to be used in the simulations.
  • proc.load defines the processor load in parallel computations.

The proc.load argument is used to define the number of processor cores dedicated to the simulations. A numeric value can be defined as well as character value which automatically detects the number of cores:

  • low: 1 processor core.

  • med: Number of available processor cores / 2.

  • high: Number of available processor cores 1.

  • full: All available processor cores.

Examples

Examples of SimParameters object specification:

Perform 10000 simulations using all available processor cores:

SimParameters(n.sims = 10000, 
              proc.load = "full", 
              seed = 42938001)

Perform 10000 simulations using 2 processor cores:

SimParameters(n.sims = 10000, 
              proc.load = 2, 
              seed = 42938001)

CSE function

Description

The CSE function is invoked to runs simulations under the Clinical Scenario Evaluation approach. This function uses four arguments:

  • data defines a DataModel object.

  • analysis defines an AnalysisModel object.

  • evaluation defines an EvaluationModel object.

  • simulation defines a SimParameters object.

Examples

The following example illustrates the use of the CSE function:

# 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 results

Once Clinical Scenario Evaluation-based simulations have been run, the CSE object returned by the CSE function contains a list with the following components:

  • 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 DataModel object.

  • analysis.structure: a list containing the analysis structure according to the AnalysisModel object.

  • evaluation.structure: a list containing the evaluation structure according to the EvaluationModel object.

  • sim.parameters: a list containing the simulation parameters according to SimParameters object.

  • timestamp: a list containing information about the start time, end time and duration of the simulation runs.

The simulation results can be summarized in the R console using the summary function:

summary(case.study1.results)

A Microsoft Word-based simulation report can be generated from the simulation results produced by the CSE function using the GenerateReport function, see Simulation report.