ADSSModExample1: Simulation-based design of an adaptive trial with sample size...

Description Arguments Value See Also Examples

Description

Consider a seamless Phase II/Phase III or Phase III trial with a normally distributed primary efficacy endpoint. A larger value of the endpoint corresponds to a more favorable outcome. A single dose or regimen of an experimental treatment will be compared to a control. An adaptive design with two interim analyses will be employed in the trial (the first interim analysis may correspond to the end of the Phase II portion of the trial). The following decision rules will be applied at the interim looks:

The following design parameters will be assumed:

In addition, the following decision rules will be considered at the two interim analyses:

Finally, the mean effects in the control and treatment arms are assumed to be equal to 0 and 0.3, respectively, with a common standard deviation of 1.

Key operating characteristics of this adaptive design will be evaluated using the ADSSMod function with 10,000 simulation runs. To invoke this function, a list of all design and decision rule parameters (parameters) needs to be created as shown below. A comprehensive simulation report can be generated by calling the GenerateReport function and a graphical user interface can be launched using the ADSSModApp function.

Arguments

No arguments

Value

No return value

See Also

ADSSMod, ADSSModExample2, ADSSModExample3

Examples

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# List of all parameters
parameters = list()

# Endpoint type
parameters$endpoint_type = "Normal"

# Direction of favorable outcome
parameters$direction = "Higher"

# Number of enrolled patients (control, treatment) 
parameters$sample_size = c(120, 120)

# Patient dropout rate
parameters$dropout_rate = 0.05

# Mean and SD in the control arm 
parameters$control_mean = 0
parameters$control_sd = 1

# Mean and SD in the treatment arm 
parameters$treatment_mean = 0.3
parameters$treatment_sd = 1

# Information fractions at IA1, IA2, FA (before sample size adjustment) 
# and FA (after sample size adjustment)
parameters$info_frac = c(0.4, 0.6, 1, 1.3)

# Futility threshold for conditional power at IA1
parameters$futility_threshold = 0.1

# Promising interval for conditional power at IA2
parameters$promising_interval = c(0.5, 0.9)

# Target conditional power for increasing the sample size at IA2
parameters$target_power = 0.9

# One-sided alpha level
parameters$alpha = 0.025

# Number of simulations, you should prefer more
parameters$nsims = 100

# Run simulations to compute operating characteristics
results = ADSSMod(parameters)

# Generate a simulation report (remove tempfile)
GenerateReport(results, 
              tempfile("ADSSMod Normally distributed endpoint.docx", fileext=".docx"))

MedianaDesigner documentation built on Oct. 11, 2021, 9:10 a.m.