SBIR-STTR Award

Software for Group Sequential Trials
Award last edited on: 7/9/07

Sponsored Program
SBIR
Awarding Agency
NIH : NCI
Total Award Amount
$941,863
Award Phase
2
Solicitation Topic Code
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Principal Investigator
Edward Lakatos

Company Information

BiostatHaven Inc

120 Truesdale Drive
Croton-on-Hudson, NY 10520
   (914) 271-7761
   ed@biostathaven.com
   www.biostathaven.com
Location: Single
Congr. District: 17
County: Westchester

Phase I

Contract Number: 1R43CA101617-01
Start Date: 00/00/00    Completed: 00/00/00
Phase I year
2003
Phase I Amount
$153,331
Group sequential trials is the term used to describe clinical trials that take place over a period of time, with statistical analyses being conducted at various points over the course the trial. If it can be shown at an interim analysis that the treatment is effective there may be an ethical imperative to stop the study at that point, and for this reason group sequential trials are mandated by the NIH for certain types of studies including many in the fields of heart disease, cancer, and AIDS. The goal of this project is to develop software for planning and monitoring group sequential trials. Like other programs, this one will work with various types of effect size, will include modules for study design as well as for interim analyses, will allow the user to work with various algorithms and with various stopping rules. However, this program will differ from what is currently available in a number of critically important ways. First, it will feature a more sophisticated and realistic survival model. Other programs force the researcher to assume that the hazard rates are constant for the duration of the study. This program, by contrast, will allow the hazard rates and treatment effects to vary over time. Additionally, it will allow researchers to incorporate such factors as non-compliance and loss to follow-up and to competing risks. This will yield substantially improved estimates of the sample size required to yield a given level of power. It will also yield better estimates of the amount of information likely to be available at each of the interim looks, which is crucial for being able to plan the timing of the DSMB meetings and total study duration. Second, the program will feature a lucid, intuitive, and informative interface for data entry and a corresponding array of options for output. Finally, it will be affordable.

Thesaurus Terms:
clinical trial, computer program /software, computer system design /evaluation, longitudinal human study, mathematical model, model design /development drug adverse effect, long term survivor

Phase II

Contract Number: 2R44CA101617-02
Start Date: 00/00/00    Completed: 00/00/00
Phase II year
2005
(last award dollars: 2006)
Phase II Amount
$788,532

The term "group-sequential" is used to describe clinical trials in which statistical analyses will be conducted at various points over the course of the trial. If it can be shown at an interim analysis that the treatment is effective, there may be an ethical imperative to terminate the trial. Both the NIH and FDA mandate this type of trial for certain studies, including many in the field of heart disease, cancer, and AIDS. The goal of this project is to develop a comprehensive statistical software package for the design and analysis of group-sequential trials. It will provide modules for the design stage and the interim analysis stage. During the design stage, the user will be able to choose from a wide variety of stopping rules, and to calculate sample size and power. During the interim analysis stage, the user will be able to plot the current level of evidence in relation to the pre-specified boundary, as well as calculate conditional power. It will differ from what is currently available in several respects. First, it will use realistic survival models to accurately determine sample size and power. Other software force the user to assume that hazard rates are constant for the duration of the trial. In contrast, this program will allow the hazard rates and treatment effect to vary over time. In addition, it will allow researchers to account for such factors as non-compliance, and loss to follow-up and competing risks, etc. This will substantially improve estimates of sample size and power. It will also yield better estimates of the amount of information likely to be available at each of the interim analyses, which is crucial to be able to plan the timing of the DMC meetings and total study duration. It will provide tables and graphs for optimizing designs