The developers of Cytel's popular East(R) clinical trials design suite now offer two East PROCs for SAS users:
PROC EastMonitor allows users to take full advantage of East's popular Interim Monitoring Dashboard.
PROC MCPMod provides clinical trial designers the chance to design trials using the modern MCPMod method.

PROC EastMonitor

Beginning in August 2014, users will have access to East’s design, simulation and monitoring tools within a SAS environment.

East® PROCs is a new tool for SAS users, which allows programmers to access the East® Interim Monitoring Dashboard within a SAS interface. SAS users can now design and monitor group sequential trials, and conduct sample-size re-estimation. Results from East® PROCs are easy to import back to East® for interactive decision-making and simulations.

Features of EastMonitor

  • Seamless conversion into SAS
  • East's data management and reporting capabilities available in SAS (e.g. audit trails)
  • Testing and adjusted inference for early stopping due to efficacy and/or futility, as well as application of the sample size re-estimation method of Cui, Hung, and Wang (1999)
  • Import results from East PROC back into East for interactive decision-making and simulations.


Ever since Multiple Comparison Procedure - Modeling (better known as the MCPMod method) obtained FDA and EMA approval, its prevalence in clinical trial design has been on the increase.

PROC MCPMod allows designers of dose-finding trials to consider the popular MCPMod method in their design of modern clinical trials.  

PROC MCPMod®: Dose-finding for Modern Clinical Trials

Moving into a Phase 3 trial with correct doses no longer requires just getting the dose right. While regulators expect top-notch researchers to understand the way new medicines work, sponsors have discovered that dose-response modeling is critical for de-risking Phase 3 trials.

Fitting the right model to your data can generate a lot of information about your therapeutic. A good model provides insights that you could easily miss. Finding such a model also means that in the event that the doses tested in Phase 3 fail, sponsors need not go back and perform further Phase 2 studies. An incorrect fit, however, can lead a trial down an expensive path towards failure.

The MCPMod method was developed to test several candidate dose-response models at once using trusted multiple comparison procedures. Based on the works of Pinheiro, Bornkamp and Bretz (2006) and Bornkamp, Bretz, Dette and Pinheiro (2011), the success of MCPMod depends on the flexibility of choosing from an assortment of candidate models, and guarding sponsors from the costly consequences of model misspecification. It allows sponsors to test a wider dose-range efficiently, providing improved data for getting dose-selection right.

[1] Pinheiro, José, Björn Bornkamp, and Frank Bretz. "Design and analysis of dose-finding studies combining multiple comparisons and modeling procedures." Journal of biopharmaceutical statistics 16.5 (2006): 639-656.
[2] Bornkamp, Björn, et al. "Response-adaptive dose-finding under model uncertainty." The Annals of Applied Statistics (2011): 1611-1631.

The Advantages of PROC MCPMod

Finally, you can access MCPMod methods through your validated production environment, in compliance with your Standard operating procedures and Good Clinical Practice. Why not let our industry leading team of software developers provide you with a validated PROC for use in SAS?

Aiming to provide flexibility, robustness, and strong Family Wise Error Rate (FWER), PROC MCPMod provides the judicious statistician with opportunities to navigate model uncertainty through rapid comparisons of multiple candidate models. Using a PROC to build your model means that you can easily submit the rationalization for your chosen model along with other clinical data stored in SAS.

Features in PROC MCPMod

  • Design dose-ranging trials with broad dose range by building PROC MCPMod into your simulation program
  • Evaluate a wide selection of candidate models using an array of model shapes
  • Guard against model misspecification
  • Generate clear dialogue between statisticians and clinicians about the nature of the dose-response curve
  • Identify the minimum effective dose (MED) that is statistically significant and produces a relevant biological effect
  • Achieve control of the Family Wise Error Rate while using Multiple Comparison Procedures
  • Analyze data using a variety of covariates and endpoints (e.g. continuous, binary and count)
  • Work with intuitive SAS syntax

PROC MCPMod allows you to perform multiple comparison procedures and modeling within Windows and UNIX environments on a range of SAS platforms. 

Related News & Events

Cytel Vice President Yannis Jemiai joined statisticians from SAS and STATA to talk software development.
PROC EastMonitor Case Study by Cytel India CEO Ajay Sathe