This case study covers a simple case with one ISA and no interim analysis (IA) to help the user become familiar with the software and the necessary elements to set-up the simulation. This package is designed with the intention that others can add new functionality as needed through the use of S3 classes. In this case study, we add a new analysis model that is not currently part of the package. As the R community for this package grows, others may add functionality to the base package.
The R code may be found in the Examples/CaseStudy2
If the goal at the end of the study is to make a decision of Go or No Go, then \(P_U = P_L\).
In the context of this case study, patient outcomes are not expected to change over the 24 weeks for the control treatment, where as the treatment is expected to decrease the values of the patient outcomes. Thus, \(\delta_C - \delta_T\) is expected to be greater than 0 for effective treatments and consequently, high values for \(\rho\) indicate an effective treatment.
The function SetupTrialDesign() is located in TrialDesign.R and the function is called to build the trial design object from the BuildMe.R file in this example.
The simulation object contains all information to specify how the trial is simulated. Specifically, it contains a list of scenarios, such as the null and alternative, details about patient accrual rates and in a multiple ISA setting information about how and when the second and subsequent ISAs are added to the trial.
For case study 2, the data for each patient is simulated from a multi-variate normal. Based on the historical data we have the following means for control and the treatment effect shown below is used to specify various scenarios in the simulation study, more detail provided below.
| Baseline | Week 4 | Week 8 | Week 12 | Week 16 | Week 20 | Week 24 | |
|---|---|---|---|---|---|---|---|
| Control | 3.5 | 3.5 | 3.5 | 3.5 | 3.5 | 3.5 | 3.5 |
| Treatment | 3.5 | 3.4 | 3.0 | 2.5 | 2.9 | 1.4 | 0.9 |
In addition, the estimates of the variance/covariance over time is
\[V = \begin{bmatrix}0.5&0.4&0.4&0.4&0.4&0.4&0.4 \\0.4&0.5&0.4&0.4&0.4&0.4&0.4 \\0.4&0.4&0.5&0.4&0.4&0.4&0.4 \\0.4&0.4&0.4&0.5&0.4&0.4&0.4 \\0.4&0.4&0.4&0.4&0.5&0.4&0.4 \\0.4&0.4&0.4&0.4&0.4&0.5&0.4 \\0.4&0.4&0.4&0.4&0.4&0.4&0.5 \\\end{bmatrix}\]
The trial recruitment is specified in terms of the number of patients accrued per month per site (patients/month/site) and the number of sites open in the platform. For simplicity, the ramp-up in the number of patients/month/site is not applied within a site and recruitment to the platform assumes a Poisson process with the rate equal to the number of patients/month/site * number of sites open.
To specify the scenarios, define the mean for control as \(M_C\), for treatment \(M_T\) defined in Table 1 above and the percent effect as \(p\) and the true mean for the treatment in a given scenario is defined as
\[ M^*_T = M_c + p *( m_T - m_C ) \]
\(( m_T - m_C)\) is the maximum treatment effect and each scenario is defined a a percent (\(p\)) of the maximum treatment effect.
The function SetupSimulations() is located in SimulationDesign.R and the function is called to build the simulation object for the BuildMe.R file in this example.