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Best Practices in Statistical Analysis: An Overview - By AtoZ Compliance
Key Take Away:
This webinar will provide an overview of considerations that should be incorporated into a study design, as well as ways to compensate or adjust for limitations in study design and deviations from protocol by focusing on best practices that adhere to FDA Guidance for Industry E9: Statistical Principals for Clinical Trials (ICH E9).
The approach taken to study design and analysis should focus on the research question and the reason for the study itself, not solely on the choice of which statistical test to use to analyze the data after it is collected. P-values can support a research hypothesis, but only tell a small part of the story. A p-value is worthless if the study falls short on the 3 D’s of: Design, Diagnostics, Description of the findings.
Statistical practice has evolved tremendously over the last 20 years. However, The ICH E9 guidance hasn’t been updated since 1998. Thus, a researcher’s idea of clinical trial Statistical Analysis plan, calling upon knowledge of new and in many cases better statistical applications, may not be seen as the best statistical practice in the eyes of a regulatory authority.
Ms. Eisenbeisz will give some examples of studies that have incorporated Bayesian techniques and other recent applications of statistical practice, both with and without success.
Why Should You Attend:
The monetary and temporal costs of conducting clinical research are enormous. Therefore, it makes sense to incorporate the best statistical practices of design, diagnostics and description of findings as early as possible in the development of a study.
Problems inevitably arise while conducting the clinical trial phases. Subjects do not complete the trial. Adverse events occur and must be properly documented and resolved, sites or subjects fall out of compliance, any number of things can and do happen.
This webinar won’t cover every problem and statistics cannot solve every problem. However, there are checks and features that can be incorporated into a study to plan for deviations from protocols and processes. Some can be incorporated into the statistical analysis plan. Other remedies can be performed during data analysis.
Areas Covered In This Webinar:
Brief overview of ICH E9 guidance
Types of Trials
Exploratory vs. Confirmatory
--Using scientific method as a guide, Elaine will explain the differences in the type of trial and what can and cannot be concluded with a statistical study.
--Not all studies have to be ITT if a research plans and designs the study to accommodate a per-protocol population. Elaine will show how to design the study to incorporate populations for efficacy and safety endpoints.
Secondary and subsequent endpoints
--Developing a proper and testable research question.
--Statistical hypotheses: You rarely explicitly seen them. But they need to be implicitly in the study plan.
Randomization and Blinding
Stratify or control variables? Learn study aspects of when one approach might be better than another.
--How the three types of trial differ in approach and analysis
Sequential Design and Interim Analysis
--Using planned statistical checks inside the study timeline to plan for the possibility of futility in a study via early stopping rules
Study Power and Sample Size
--Brief overview of what is needed to properly power a study for an adequate sample to observe significant effects
Data Cleaning and Coding
--The GIGO principle (Garbage in, Garbage out) and how to avoid it
--Backup, backup, backup
--P-values are grand. Effect sizes are better
Evaluation of Safety and Adverse Events
--MEDRA classification system
--Testing for differences in AE’s between study groups
Versioning of Reports
Tables and Figures
• Considerations that should be incorporated into a study design
• How to compensate or adjust for limitations in study design and deviations from protocol
• Best practices that adhere to FDA Guidance for Industry E9: Statistical Principals for Clinical Trials (ICH E9)
• Know the 3 D’s of: Design, Diagnostics, Description of the findings
Who Will Benefit:
• Clinical Trail Sponsors
• Clinical Personnel who handle CRF and data collection
• Statisticians new to the field of Clinical Research
Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 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 state-wide 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 received her Master’s Certification in Applied Statistcs from Texas A&M, and is currently finishing her graduate studies at Rochester Institute of Technology.
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Contact the event managers listed below for more information about how you can participate at the Best Practices in Statistical Analysis: An Overview - By AtoZ Compliance.
Clinical Trail Sponsors
Clinical Personnel who handle CRF and data collection
Statisticians new to the field of Clinical Research
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