Mediana (version 1.0.8)

OutcomeDist: OutcomeDist object

Description

This function creates an object of class OutcomeDist which can be added to an object of class DataModel.

Usage

OutcomeDist(outcome.dist, outcome.type = NULL)

Arguments

outcome.dist

defines the outcome distribution.

outcome.type

defines the outcome type.

Details

Objects of class OutcomeDist are used in objects of class DataModel to specify the outcome distribution of the generated data. A single object of class OutcomeDist can be added to an object of class DataModel.

Several distribution are already implemented in the Mediana package (listed below, along with the required parameters to specify in the outcome.par argument of the Sample object) to be used in the outcome.dist argument:

• UniformDist: generate data following a univariate distribution. Required parameter: max.

• NormalDist: generate data following a normal distribution. Required parameters: mean and sd.

• BinomDist: generate data following a binomial distribution. Required parameter: prop.

• BetaDist: generate data following a beta distribution. Required parameter: a. and b.

• ExpoDist: generate data following an exponential distribution. Required parameter: rate.

• WeibullDist: generate data following a weibull distribution. Required parameter: shape and scale.

• TruncatedExpoDist: generate data following a truncated exponential distribution. Required parameter: rate and trunc.

• PoissonDist: generate data following a Poisson distribution. Required parameter: lambda.

• NegBinomDist: generate data following a negative binomial distribution. Required parameters: dispersion and mean.

• MultinomialDist: generate data following a multinomial distribution. Required parameter: prob.

• MVNormalDist: generate data following a multivariate normal distribution. Required parameters: par and corr. For each generated endpoint, the par parameter must contain the required parameters mean and sd. The corr parameter specifies the correlation matrix for the endpoints.

• MVBinomDist: generate data following a multivariate binomial distribution. Required parameters: par and corr.For each generated endpoint, the par parameter must contain the required parameter prop. The corr parameter specifies the correlation matrix for the endpoints.

• MVExpoDist: generate data following a multivariate exponential distribution. Required parameters: par and corr. For each generated endpoint, the par parameter must contain the required parameter rate. The corr parameter specifies the correlation matrix for the endpoints.

• MVExpoPFSOSDist: generate data following a multivariate exponential distribution to generate PFS and OS endpoints. The PFS value is imputed to the OS value if the latter occurs earlier. Required parameters: par and corr. For each generated endpoint, the par parameter must contain the required parameter rate. The corr parameter specifies the correlation matrix for the endpoints.

• MVMixedDist: generate data following a multivariate mixed distribution. Required parameters: type, par and corr. The type parameter can take the following values:

  • NormalDist

  • BinomDist

  • ExpoDist

  For each generated endpoint, the par parameter must contain the required parameters according to the type of distribution. The corr parameter specifies the correlation matrix for the endpoints.

The outcome.type argument defines the outcome's type. This argument accepts only two values:

• standard: for fixed design setting.

• event: for event-driven design setting.

The outcome's type must be defined for each endpoint in case of multivariate disribution, e.g. c("event","event") in case of multivariate exponential distribution.

References

http://gpaux.github.io/Mediana/

See Also

See Also DataModel.

Examples

# NOT RUN {
# Simple example with a univariate distribution
# 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
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))

# Complex example with multivariate distribution following a Binomial and a Normal distribution
# Variable types
var.type = list("BinomDist", "NormalDist")

# Outcome distribution parameters
plac.par = list(list(prop = 0.3), list(mean = -0.10, sd = 0.5))

dosel.par1 = list(list(prop = 0.40), list(mean = -0.20, sd = 0.5))
dosel.par2 = list(list(prop = 0.45), list(mean = -0.25, sd = 0.5))
dosel.par3 = list(list(prop = 0.50), list(mean = -0.30, sd = 0.5))

doseh.par1 = list(list(prop = 0.50), list(mean = -0.30, sd = 0.5))
doseh.par2 = list(list(prop = 0.55), list(mean = -0.35, sd = 0.5))
doseh.par3 = list(list(prop = 0.60), list(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 = list(type = var.type, par = plac.par, corr = corr.matrix)
outcome1.dosel = list(type = var.type, par = dosel.par1, corr = corr.matrix)
outcome1.doseh = list(type = var.type, par = doseh.par1, corr = corr.matrix)

# Outcome parameter set 2
outcome2.plac = list(type = var.type, par = plac.par, corr = corr.matrix)
outcome2.dosel = list(type = var.type, par = dosel.par2, corr = corr.matrix)
outcome2.doseh = list(type = var.type, par = doseh.par2, corr = corr.matrix)

# Outcome parameter set 3
outcome3.plac = list(type = var.type, par = plac.par, corr = corr.matrix)
outcome3.doseh = list(type = var.type, par = doseh.par3, corr = corr.matrix)
outcome3.dosel = list(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))
# }