Consider a Phase II or Phase III trial with three treatment arms that correspond to two doses of an experimental treatment versus a control and assume that the primary efficacy endpoint is a binary endpoint (response rate, i.e., a higher value indicates a more favorable outcome). A futility assessment will be performed for each treatment arm at an interim look using conditional power and a treatment arm will be dropped if conditional power is below a pre-defined futility threshold. An optimal value of the futility threshold can be found by computing the sensitivity and specificity rates associated with the futility stopping rule and then identifying the threshold that simultaneously maximizes both rates.
The following design parameters will be assumed in the trial:
A balanced design with 75 enrolled patients per trial arm will be utilized.
The patient dropout rate at the end of the treatment period is assumed to be 5%.
An early interim analysis with the information fraction of 30% will be conducted.
The calculations will be performed under the following set of treatment effect assumptions:
The control response rate is set to 35%.
The response rates in the three treatment arms are set to 45%, 50% and 55%.
Operating characteristics of the futility stopping rule (sensitivity and specificity rates) and an optimal futility threshold will be computed using the
FutRule function based on 1,000 simulation runs. A list of all trial design parameters (
parameters) needs to be set up as shown below and passed to this function. A detailed simulation report can be generated using the
GenerateReport function and a graphical user interface can be launched by calling the
No return value
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
# List of all parameters parameters = list() # Endpoint type parameters$endpoint_type = "Binary" # Direction of favorable outcome parameters$direction = "Higher" # Number of enrolled patients (control, three treatments) parameters$sample_size = c(75, 75, 75, 75) # Dropout rate parameters$dropout_rate = 0.05 # Response rate in the control arm parameters$control_rate = 0.35 # Response rates in the treatment arms parameters$treatment_rate = c(0.45, 0.5, 0.55) # Information fraction parameters$info_frac = 0.3 # One-sided alpha level parameters$alpha = 0.025 # Number of simulations, you should prefer more parameters$nsims = 100 # Run simulations to compute characteristics of the futility stopping rule results = FutRule(parameters) # Generate a simulation report (remove tempfile) GenerateReport(results, tempfile("FutRule Binary endpoint.docx", fileext=".docx"))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.