ADPopSelExample1: Simulation-based design of an adaptive trial with population...

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 this endpoint indicates a more favorable outcome). The efficacy and safety profiles of a single dose or regimen of an experimental treatment will be evaluated versus a control in two patient populations, namely, the overall population of patients and a pre-defined subset of patients with a biomarker-positive status. An adaptive design with two interim analyses will be used in the trial (the first interim analysis may correspond to the end of the Phase II portion of the trial) to support the following decision rules:

The following design parameters will be assumed:

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

The treatment effect assumptions need to be specified within the biomarker-positive population as well as the complementary population (biomarker-negative population). A common effect will be assumed in the control arm, i.e., the mean and standard deviation of the primary efficacy endpoint will be 0 and 1, respectively, in the biomarker-positive and biomarker-negative populations. A stronger treatment effect will be assumed in the biomarker-positive subset compared to the complementary subset, i.e., the mean of 0.4 in biomarker-positive patients and the mean 0.25 in biomarker-negative patients with a common standard deviation of 1.

Key operating characteristics of the proposed adaptive design with population selection will be evaluated using the ADPopSel function based on 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 for this adaptive design can be generated by calling the GenerateReport function and a graphical user interface can be launched using the ADPopSelApp function.

Arguments

No arguments

Value

No return value

See Also

ADPopSel, ADPopSelExample2, ADPopSelExample3

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
# 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(220, 220)

# Prevalence of biomarker-positive patients in the overall population
parameters$prevalence = 0.5

# Patient dropout rate
parameters$dropout_rate = 0.1

# Mean and SD in the control arm (biomarker-negative, biomarker-positive)
parameters$control_mean = c(0, 0)
parameters$control_sd = c(1, 1)

# Mean and SD in the treatment arm (biomarker-negative, biomarker-positive)
parameters$treatment_mean = c(0.25, 0.4)
parameters$treatment_sd = c(1, 1)

# Information fractions at IA1, IA2, FA
parameters$info_frac = c(0.4, 0.6, 1)

# Futility threshold for conditional power at IA1
parameters$futility_threshold = 0.2

# Influence threshold at IA2
parameters$influence = 0.1

# Interaction threshold at IA2
parameters$interaction = 1.3

# 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 = ADPopSel(parameters)

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

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