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A set of slides with annotated examples explaining how
to use the CTS,
and describing
its features.

.: What is the Clinical Trial Simulator (CTS)?

It is an integrated system of several statistical, methodological, and display software packages that are used to determine and display the consequences of the different decisions clinical trialists might make about the participants, treatments, and conduct of their randomized clinical trial (RCT). The specific simulator you are about to use is named IcebergSim (because 9/10ths of what it will do for you is conducted below the surface!).

.: Pragmatic Randomized Controlled Trials In Health Care
This program is one of the tools been developed by PRACTIHC. Please visit the PRACTIHC website for more information.

.: How does it work?

Clinical trialists simply enter the decisions and estimates they have made about the event rates they anticipate among experimental and control patients, and either the number of patients they think they can enrol or the statistical power they’d like to achieve.
But their options don’t stop there. Users can also specify the proportions of patients they fear might be lost-to-follow-up or might not comply with their treatments.
Moreover, they can tailor-make statistical stopping (or “warning”) rules for helping to decide whether to stop their trial early if its emerging results are extreme.
Then, a simple push of the “GO” button triggers 5,000 Monte Carlo simulations of the trial they designed, and all sorts of results are displayed.
Finally, after examining these results, users can go back and change any or all of their original inputs and play “What if . . . ?“ to their hearts’ content.

.: What can it be used for?

  1. To help you learn how to design, analyse, and report RCTs (its outputs follow the CONSORT recommendations).
  2. And, while doing so, to show you the effects of the “real world” problems that are likely to occur when you carry out the “ideal” RCT you’ve designed:
    • when you can’t recruit as many patients as you planned,
    • when the event rate for control patients is lower than you thought,
    • when you don’t achieve the risk-reduction you were hoping for,
    • when some patients are lost-to-follow-up,
    • when other patients don’t comply with their assigned treatments,
    • etc.
  3. To help you understand the dynamic interactions between the design elements of an RCT, and how changing just one of them affects several of the others: what Dave Sackett calls “Physiological Statistics”. (CMAJ 2001;165:1226-37, or Pp 201-217 in the 3rd Edition of the Clinical Epidemiology Text on How to Do Clinical Practice Research – ISBN 0-7817-4524-1).

.: Funding

The development of this simulator was partially supported by PRACTIHC with funding from the European Commission's 5th Framework international collaboration with Developing Countries, Research Contract. ICA4-CT-2001-10019, and by the Global Health Research Initiative (GHRI) of the Canadian Institutes of Health Research (CIHR).

.: Credits

The simulator was inspired on a trial simulator developed by D.W. Taylor, E.G. Bosh and D. Sacket in 1990. (D.W. Taylor, E.G. Bosch. CTS: a clinical trials simulator, Statistics in Medicine, 9:787-801,1990). [MEDLINE]

IcebergSim was conceived by Eduardo Bergel and Dave Sackett in Havana in 2003, and you are using the Beta-test version of its 4th generation.
Eduardo Bergel continues to be its main developer, now joined by Luz Gibbons in developing the Cluster and the Stopping Rules Modules. Alvaro Ciganda participates in software development.
The IcebergSim logo was designed by Steve Janzen.

Marcelo Delgado, Alvaro Ciganda and Martin Silva participated in the development of the previous version (v 1.0).  

The software was developed using the python programming language, with the qt GUI toolkit. Graphics were developed using and external, free graphic library (PLOTICUS).


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Latest update of this page 09/23/2010 -- Latest IcebergSim beta version: 4.03 (09/23/2010)
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