Data Stack

DataStack function

The DataStack function can be used to get the data generated in the Clinical Scenario Evaluation. The generation of the data stack is based on the pre-defined DataModel and SimParameters objects.

Description

Inputs

The DataStack function requires the input of two pre-specified objects defined in the following two arguments:

  • data.model defines a DataModel object, as described in this page.

  • sim.parameters defines a SimParameters object, as described in this page.

Outputs

The DataStack function returns a DataStack object containing a list with the following components:

  • description: a description of the object.

  • data.set: a list of size n.sims defined in the SimParameters object. This list contains the data generated for each data scenario (data.scenario level) and each sample (sample level). The data generated for the ith simulation run, the jth data scenario and the kth sample is stored in data.stack$data.set[[i]]$data.scenario[[j]]$sample[[k]] where data.stack is the DataStack object.

  • data.scenario.grid: a data frame indicating all data scenarios according to the DataModel object.

  • data.structure: a list containing the data structure according to the DataModel object.

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

Example

The following example illustrates the use of the DataStack function.

In this example, we will use the data model and simulation parameters specified in the Case study 1 for normally distributed endpoints (see Case study 1).

Specification of the DataModel object

The data model is specified below:

# 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))

Specification of the SimParameters object

Additionally, the simulation parameters are specified:

# Simulation Parameters
case.study1.sim.parameters = SimParameters(n.sims = 1000, proc.load = 2, seed = 42938001)

Data generation

The data generated in the CSE function can be obtained using the DataStack function as follows:

# Generate data
case.study1.data.stack = DataStack(data.model = case.study1.data.model,
                                   sim.parameters = case.study1.sim.parameters)

Manipulation of a DataStack object

Once the DataStack object has been generated, the user can manipulate the object easily as it is structure as a list object. Basically, the data sets are stored in the data.set level of the object. This length of the list corresponds to the number of simulations specified in the SimParameters object.

# Print all data sets generated for all simulations
case.study1.data.stack$data.set

Within each simulation run, a nested list is used to store the data set for each data scenario and each sample. For example, the user can access to the data generated in the 100th simulation run for the 2nd data scenario as follows:

# Print the data set generated in the 100th simulation run for the 2nd data scenario
case.study1.data.stack$data.set[[100]]$data.scenario[[2]]

Finally, the data set generated for the first sample defined in the data model is accessible as follows:

# Print the data set generated in the 100th simulation run for the 2nd data scenario for the first sample
case.study1.data.stack$data.set[[100]]$data.scenario[[2]]$sample[[1]]

ExtractDataStack function

To ease the manipulation of a DataStack object, a function has been created to extract a particular set of data stack according to the data scenario, sample id and simulation runs index.

Description

Inputs

The ExtractDataStack function requires the input of a pre-specified object defined in the following two arguments:

  • data.stack defines a DataStack object, as described above.

  • data.scenario defines the data scenario index to extract. By default all data scenarios will be extracted.

  • sample.id defines the sample id to extract. By default all sample ids will be extracted.

  • simulation.run defines the simulation run index. By default all simulation runs will be extracted.

Outputs

The ExtractDataStack function returns a list having the same structure as the data.set argument of a DataStack object

  • data.set: a list of size corresponding to the number of simulation runs specified by the user defined in the simulation.run argument. This list contains the data generated for each data scenario (data.scenario argument) and each sample (sample.id argument) specified by the user.

Example

Returning to our example, the user can extract the data set generated for the 100th simulation run and the 2nd data scenario as follows:

case.study1.extracted.data.stack = ExtractDataStack(data.stack = case.study1.data.stack,
                                                    data.scenario = 2,
                                                    simulation.run = 100)

A carefull attention should be paid on the index of the nested lists. As only one simulation run index has been specified in this example, the result for the 100th simulation runs is stored in the first level of the list (case.study1.extracted.data.stack$data.set[[1]]). Similarly, as only one data scenario has been requested, the result for the 2nd data scenario is now in the first position (case.study1.extracted.data.stack$data.set[[1]]$data.scenario[[1]]).