Division of Quantitative Sciences - Department of Biostatistics

Software Download Site
 File NameSizeNotes
 MultcLean_V2.1.0_WithFX4.0.zip  62133 KB Includes Microsoft .Net Framework 4.0
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Multc Lean

Multc Lean is for the design of single arm clinical trials monitoring response (efficacy) and toxicity (safety). These are typically phase II clinical trials.

Requirements

  • Windows 7
    (other Windows versions may be compatible but this has not been tested)
  • Administrative permissions may be required to install Multc Lean Desktop depending on the chosen installation location.
  • The following packages will be installed if they are not present:
    • Microsoft .Net 4.0 Framework
    • Microsoft Visual C++ 2010 x86 Redistributable 10.0.40219
    • Microsoft Windows Installer 3.1
  • To view the Multc Lean user's guide and statistical tutorial, a PDF file viewer (not included with the software) such as Adobe Reader (available for free here) must be installed.
  • To view the example protocol memo, Microsoft Word 2010 or later, or another program which can display Microsoft Word 2010 files (not included with the software) must be installed.
  • To follow the "Send feed back via email" link in the Help -> About Multc Lean Desktop ... window, an email client such as Microsoft Outlook (not included with the software) must be installed.

What's New in Version 2.1

  • The standard treatment event rate may be modeled as a constant rate instead of a random variable with a Beta distribution
  • Updated documentation, including a new example protocol memo
  • Significant improvements to the user interface
  • Minor bug fixes
  • Occasionally sends usage statistics and crash reports to our biostatistics software support team to improve your experience using the software.

What's New in Version 2.0

  • Improved display of stopping boundaries (details here)
  • Updated documentation

Description

Multc Lean implements a special case of the single-arm safety monitoring method of Thall, Simon, and Estey [1].  The method of Thall, Simon, and Estey is a general family of Bayesian designs for monitoring phase II trials. The method of Thall, Simon, and Estey can be used to monitor any number of outcomes.

The Multc Lean software only monitors two outcomes. This is a limitation of (or simplification provided by) the software implementation, not the statistical method.

The Multc Lean software also allows a modification to the method, whereby the standard treatment rate may be modeled by a fixed constant rate, instead of a random variable with a Beta distribution.

Features

  • A Windows 7 user interface
  • Extensive documentation
  • The ability to simulate trial duration
  • Support for continuous monitoring or monitoring in cohorts
  • Support for a minimum number of patients to treat before evaluating the stopping rules
  • Support for modeling standard treatment rates as constant rates instead of random variables with a Beta distribution
  • A “lean” implementation which is very easy to use

Multc Lean Desktop Screenshot

Why Bayesian Trial Designs?

An advantage of Bayesian methods is that they allow arbitrary sample sizes. One starts with an uninformative probability distribution on the parameters of interest (such as the probabilities of toxicity and response) and ends with a more informative posterior distribution after the trial. There is no all-or-nothing threshold where n patients are too few but n+1 patients are plenty. The uncertainty in the posterior parameter estimates decreases continuously as the number of patients in the trial increases. Small studies are not disallowed; they simply have more posterior uncertainty than larger studies.

Documentation

An extensive statistical tutorial is provided with the software. This tutorial covers the statistical basis of the method, has guidelines for how to use the method and includes exercises and solutions. See also the Multc Lean user's guide included with the software.

An example protocol memo is provided with the software. This is intended to help those, often statisticians, who are designing clinical trials using Multc Lean Desktop to communicate the design to colleagues. As such it is an example only, and would need to be heavily edited to match any new design. By no means do we mean to imply that this is the best or only way to communicate a design, but we hope it may be helpful.

Conducting a trial designed by this method does not require software since the stopping conditions can be tabulated before the trial begins. Here is a document commenting on the logistics of running a Multc trial for the benefit of the person responsible for monitoring the stopping rules.

Alternative Software

Multc Lean was designed to have a “lean” implementation which is very easy to use and retains only the most commonly used features of the original Multc99 program. Multc99 allows one to monitor any practical number of events. Multc99 is a command-line menu-driven text-based program with limited documentation but more flexibility than Multc Lean Desktop.

Multc Lean Desktop version 2.1 now allows you to design trials with the same stopping criteria as Multc99's “Phase IIA with Binary Outcome” designs, which are modifications of the original designs proposed by Thall, Simon, and Estey [1].  These designs, which model the standard treatment event rate as a fixed constant instead of a random variable with a Beta distribution, are some of the most frequently chosen designs supported by Multc99. Multc Lean also allows extensions to Multc99's “Phase IIA with Binary Outcome” designs, such as the ability to use a cohort size greater than one, to obtain operating characteristics when monitoring both response and toxicity events, and to simulate the average trial duration.

Credits

Hoang Nguyen developed the original MultcLean program using Microsoft C#.  John Venier added the enhancements present in versions 2.0 and 2.1.

John Cook and Hoang Nguyen implemented the numerical algorithms using Microsoft Visual C++.

References

[1] Peter Thall, Richard Simon, and Elihu Estey in “Bayesian sequential monitoring designs for single-arm clinical trials with multiple outcomes”, Statistics in Medicine, vol 14, 357-379 (1995)