Mediana (version 1.0.3)

Mediana-package: Clinical Trial Simulations

Description

Provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.

Details

Package:

Mediana

Type:

Package

Version:

1.0.3

Date:

2016-08-25

License:

GPL-2

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/

Examples

## Not run: 
# # Clinical trial in patients with rheumatoid arthritis
# 
# # Variable types
# var.type = parameters("BinomDist", "NormalDist")
# 
# # Outcome distribution parameters
# plac.par = parameters(parameters(prop = 0.3),
#                       parameters(mean = -0.10, sd = 0.5))
# 
# dosel.par1 = parameters(parameters(prop = 0.40),
#                         parameters(mean = -0.20, sd = 0.5))
# dosel.par2 = parameters(parameters(prop = 0.45),
#                         parameters(mean = -0.25, sd = 0.5))
# dosel.par3 = parameters(parameters(prop = 0.50),
#                         parameters(mean = -0.30, sd = 0.5))
# 
# doseh.par1 = parameters(parameters(prop = 0.50),
#                         parameters(mean = -0.30, sd = 0.5))
# doseh.par2 = parameters(parameters(prop = 0.55),
#                         parameters(mean = -0.35, sd = 0.5))
# doseh.par3 = parameters(parameters(prop = 0.60),
#                         parameters(mean = -0.40, sd = 0.5))
# 
# # Correlation between two endpoints
# corr.matrix = matrix(c(1.0, 0.5,
#                        0.5, 1.0), 2, 2)
# 
# # Outcome parameter set 1
# outcome1.plac = parameters(type = var.type,
#                            par = plac.par,
#                            corr = corr.matrix)
# outcome1.dosel = parameters(type = var.type,
#                             par = dosel.par1,
#                             corr = corr.matrix)
# outcome1.doseh = parameters(type = var.type,
#                             par = doseh.par1,
#                             corr = corr.matrix)
# 
# # Outcome parameter set 2
# outcome2.plac = parameters(type = var.type,
#                            par = plac.par,
#                            corr = corr.matrix)
# outcome2.dosel = parameters(type = var.type,
#                             par = dosel.par2,
#                             corr = corr.matrix)
# outcome2.doseh = parameters(type = var.type,
#                             par = doseh.par2,
#                             corr = corr.matrix)
# 
# # Outcome parameter set 3
# outcome3.plac = parameters(type = var.type,
#                            par = plac.par,
#                            corr = corr.matrix)
# outcome3.doseh = parameters(type = var.type,
#                             par = doseh.par3,
#                             corr = corr.matrix)
# outcome3.dosel = parameters(type = var.type,
#                             par = dosel.par3,
#                             corr = corr.matrix)
# 
# # Data model
# data.model = DataModel() +
#   OutcomeDist(outcome.dist = "MVMixedDist") +
#   SampleSize(c(100, 120)) +
#   Sample(id = list("Plac ACR20", "Plac HAQ-DI"),
#          outcome.par = parameters(outcome1.plac, outcome2.plac, outcome3.plac)) +
#   Sample(id = list("DoseL ACR20", "DoseL HAQ-DI"),
#          outcome.par = parameters(outcome1.dosel, outcome2.dosel, outcome3.dosel)) +
#   Sample(id = list("DoseH ACR20", "DoseH HAQ-DI"),
#          outcome.par = parameters(outcome1.doseh, outcome2.doseh, outcome3.doseh))
# 
# family = families(family1 = c(1, 2), family2 = c(3, 4))
# component.procedure = families(family1 ="HolmAdj", family2 = "HolmAdj")
# gamma = families(family1 = 0.8, family2 = 1)
# 
# # Tests to which the multiplicity adjustment will be applied
# test.list = tests("Pl vs DoseH - ACR20",
#                   "Pl vs DoseL - ACR20",
#                   "Pl vs DoseH - HAQ-DI",
#                   "Pl vs DoseL - HAQ-DI")
# 
# # Analysis model
# analysis.model = AnalysisModel() +
#   MultAdjProc(proc = "MultipleSequenceGatekeepingAdj",
#               par = parameters(family = family,
#                                proc = component.procedure,
#                                gamma = gamma),
#               tests = test.list) +
#   Test(id = "Pl vs DoseL - ACR20",
#        method = "PropTest",
#        samples = samples("Plac ACR20", "DoseL ACR20")) +
#   Test(id = "Pl vs DoseH - ACR20",
#        method = "PropTest",
#        samples = samples("Plac ACR20", "DoseH ACR20")) +
#   Test(id = "Pl vs DoseL - HAQ-DI",
#        method = "TTest",
#        samples = samples("DoseL HAQ-DI", "Plac HAQ-DI")) +
#   Test(id = "Pl vs DoseH - HAQ-DI",
#        method = "TTest",
#        samples = samples("DoseH HAQ-DI", "Plac HAQ-DI"))
# 
# # Evaluation model
# evaluation.model = EvaluationModel() +
#   Criterion(id = "Marginal power",
#             method = "MarginalPower",
#             tests = tests("Pl vs DoseL - ACR20",
#                           "Pl vs DoseH - ACR20",
#                           "Pl vs DoseL - HAQ-DI",
#                           "Pl vs DoseH - HAQ-DI"),
#             labels = c("Pl vs DoseL - ACR20",
#                        "Pl vs DoseH - ACR20",
#                        "Pl vs DoseL - HAQ-DI",
#                        "Pl vs DoseH - HAQ-DI"),
#             par = parameters(alpha = 0.025)) +
#   Criterion(id = "Disjunctive power - ACR20",
#             method = "DisjunctivePower",
#             tests = tests("Pl vs DoseL - ACR20",
#                           "Pl vs DoseH - ACR20"),
#             labels = "Disjunctive power - ACR20",
#             par = parameters(alpha = 0.025)) +
#   Criterion(id = "Disjunctive power - HAQ-DI",
#             method = "DisjunctivePower",
#             tests = tests("Pl vs DoseL - HAQ-DI",
#                           "Pl vs DoseH - HAQ-DI"),
#             labels = "Disjunctive power - HAQ-DI",
#             par = parameters(alpha = 0.025))
# 
# # Simulation Parameters
# sim.parameters =  SimParameters(n.sims = 1000, proc.load = 2, seed = 42938001)
# 
# # Perform clinical scenario evaluation
# results = CSE(data.model,
#               analysis.model,
#               evaluation.model,
#               sim.parameters)
# 
# # Reporting
# presentation.model = PresentationModel() +
#   Project(username = "[Mediana's User]",
#           title = "Case study",
#           description = "Clinical trial in patients with rheumatoid arthritis") +
#   Section(by = c("outcome.parameter")) +
#   Table(by = c("multiplicity.adjustment")) +
#   CustomLabel(param = "sample.size",
#               label = paste0("N = ", c(100, 120)))
# 
# # Report Generation
# GenerateReport(presentation.model = presentation.model,
#                cse.results = results,
#                report.filename = "Case study.docx")
# 
# ## End(Not run)