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).
Overview:
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.
Another problem is with the current guidance for industry. Regulatory guidelines are outdated (The ICH E9 hasn’t been updated since 1998) and also ambiguous on many details of statistical applications in clinical research. Proper procedures related to randomization schemas, data management strategies, and documentation of data processing are imperative to substantiate the rigor required by regulatory authorities. Additionally, these elements of data handling and documentation are essential study replication, which is paramount in scientific research.
The information presented in this webinar will allow a researcher to understand the need for proper study design and implementation. This knowledge will help the researcher to meet the requirements of regulatory authorities while keeping within the guidelines of best statistical practice.
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.
Study Population
Intention-to-treat (ITT)
Per-protocol (PP)
--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.
Endpoints
Primary 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.
Trial Designs
Superiority
Equivalence
Non-Inferiority
--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 Management
Diagnostics
Data Cleaning and Coding
--The GIGO principle (Garbage in, Garbage out) and how to avoid it
Storage
--Backup, backup, backup
Analysis
--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
Data Presentation
Versioning of Reports
Tables and Figures
Learning Objectives:
• 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:
• CRO
• Clinical Trail Sponsors
• Investigators
• Clinical Personnel who handle CRF and data collection
• Statisticians new to the field of Clinical Research
For more information, please visit : https://www.atozcompliance.com/trainings-webinar/life-sciences/pharmaceuticals/best-practices-in-statistical-analysis/elaine-eisenbeisz/300146
Email: support@atozcompliance.com
Toll Free: +1- 844-414-1400
Tel: +1-516-900-5509
Level:
Beginner
Speakers Profile:
Elaine Eisenbeisz
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. Elaine is a member in good standing with the American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.
Elaine has designed the methodology for numerous studies in the clinical, biotech, and health care fields. She currently is an investigator on approximately 10 proton therapy clinical trials for Proton Collaborative Group, based in Illinois. She also designs and analyzes studies as a contract statistician for nutriceutical and fitness studies with QPS, a CRO based in Delaware.
Elaine has also worked as a contract statistician with numerous private researchers and biotech start-ups as well as with larger companies such as Allergan and Rio Tinto Minerals. Not only is Elaine well versed in statistical methodology and analysis, she works well with project teams. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals.
