Overview:
Clinical trials are expensive, time-consuming, and labor-intensive. And in the traditional sense, study designs are inflexible.
Adaptive study designs allow for flexibility during a clinical trial. Options can be built into a study to use data collected that has accumulated at interim time points to:
The U.S. Food and Drug Administration (FDA) and other regulatory agencies require the minimization of bias in study design and analysis. In order to minimize bias, particular steps and safeguards, using regulatory guidance and sound statistical principles, must be put into place to assure validity of an clinical.
Therefore a number of considerations must be made in the design of an adaptive trial to enhance flexibility while minimizing bias, and ensuring statistically valid and well-informed decisions. Problems can arise in an adaptive design due to selection bias, interim analysis “look-sies”, and when merging dose selection and confirmation phases into one trial.
The benefits of a clinical trial with an adaptive design include savings in both time and dollars, to the desired end of bringing useful drug treatments and devices to patients more quickly.
The role of statistics in clinical trials incorporates the tools used to develop a robust study, minimize bias, and assess efficacy of new treatments as relates to comparison to competing therapies. The objective of the seminar is to provide information that can be used immediately by personnel involved in the design and analysis of clinical trials. The presentation involves use of statistical techniques and a basic understanding of statistical theory and the framework of randomized controlled trials is desired. However, presentation of statistical theory and application will be limited to only what is needed by the attendees to understand and implement adaptive trial design and analysis.
Clinical trials are expensive, time-consuming, and labor-intensive. And in the traditional sense, study designs are inflexible.
Adaptive study designs allow for flexibility during a clinical trial. Options can be built into a study to use data collected that has accumulated at interim time points to:
- Adjust sample sizes in treatment arms or reduce patient recruitment
- Drop treatment arms entirely (treatment pruning)
- Adjust randomization schema
- Combine Phase II and Phase III (adaptive seamless design)
- Early stopping of a study for futility or success
The U.S. Food and Drug Administration (FDA) and other regulatory agencies require the minimization of bias in study design and analysis. In order to minimize bias, particular steps and safeguards, using regulatory guidance and sound statistical principles, must be put into place to assure validity of an clinical.
Therefore a number of considerations must be made in the design of an adaptive trial to enhance flexibility while minimizing bias, and ensuring statistically valid and well-informed decisions. Problems can arise in an adaptive design due to selection bias, interim analysis “look-sies”, and when merging dose selection and confirmation phases into one trial.
The benefits of a clinical trial with an adaptive design include savings in both time and dollars, to the desired end of bringing useful drug treatments and devices to patients more quickly.
The role of statistics in clinical trials incorporates the tools used to develop a robust study, minimize bias, and assess efficacy of new treatments as relates to comparison to competing therapies. The objective of the seminar is to provide information that can be used immediately by personnel involved in the design and analysis of clinical trials. The presentation involves use of statistical techniques and a basic understanding of statistical theory and the framework of randomized controlled trials is desired. However, presentation of statistical theory and application will be limited to only what is needed by the attendees to understand and implement adaptive trial design and analysis.
Agenda
- Overview of Applications for Adaptive Design in Clinical Trials
- Statistical Techniques of Adaptive Design
- Practical Considerations for implementation of an Adaptive Design
- Sample Size Re-Estimation (SSR)
- Regulatory Aspects of an Adaptive Design
- When an Adaptive Design is, and is not, Appropriate
- Examples of Clinical Trials that Incorporated Adaptive Design
- Computer Code in R Statistical Software for Simulation Studies
Speakers
Elaine Eisenbeisz is a private practice consultant based in Southern California. She has over 20 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.In addition to her technical expertise, Elaine possesses a talent for conveying statistical concepts and results in a way that people can intuitively understand.
Elaine’s love of numbers began in elementary school where she placed in regional and statewide mathematics competitions. She attended University of California, Riverside, as a National Science Foundation Scholar, where she earned a B.S. in Statistics with a minor in Quantitative Management, Accounting. Elaine completed her graduate certification in Applied Statistics with Texas A & M University. Gig ‘em Aggies! Currently, she is finishing her graduate work in Applied Statistics at Rochester Institute of Technology.
Elaine is a member of The American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Elaine is also a member in good standing with the Better Business Bureau.
Current areas of interest include Bayesian inference, simulation and bootstrapping techniques, and predictive modeling.
When she isn’t crunching numbers you can find Elaine digging in her garden, playing her violin, cooking, or playing board games with friends.
Who Should Attend
- Trial Sponsors
- Principal Investigators
- Clinical Investigators
- Clinical Research Statisticians
- Clinical Research Coordinators
- Clinical Research Nurse Coordinators
- Clinical Research Associates/Assistants
- Clinical Project Managers/Leaders
- Study Managers
- Regulatory Professionals/Coordinators
- Medical Writers who need to interpret statistical reports
