Elaine Eisenbeisz, Owner and Principal Statistician of Omega Statistics, is available for training and workshops in statistics, clinical research, process control/quality assurance, and other topics related to statistics and good research practices. Please call or email the Omega Statistics offices to request the latest brochure and pricing for the event(s) that interests you.
Current offerings and scheduled events are listed below.
In addition to the current offerings listed below, Elaine can design an in-house training or virtual webinar to meet your specific needs. Just give her a call at 877-461-7226 or fill out the contact form on this page to leave her a message and she’ll contact you to schedule a time to discuss your needs and how she can help!
Omega Statistics has contracted with 3rd party companies to present the following fee based events. The events are listed in chronological order. Please click on the links that interest you to learn more and to register.
June 13th and 14th, 2019: Chicago, IL
This 2-day seminar includes the steps and techniques used to quantify variability in manufacturing processes, and to assure quality products.
All processes exhibit intrinsic variation. However, sometimes the variation is excessive and this hinders the ability to achieve reliable measurements and desired results. Statistical process control (SPC) and statistical quality control (SQC) allow us to control the functions of our processes (input) and the quality of our product (output) by providing tangible tools for monitoring and testing.
The concepts and information presented will be mainly concerned with statistical quality control: obtaining information (data) that is objective, unbiased, and useful for decision making. An emphasis will be placed on the set-up and use of control charts and acceptance sampling systems and procedures.
The objective of the seminar is to provide information that can be used immediately by personnel involved in production operations, and by supervisors and management in decision making. Although the presentation involves use of statistical techniques, presentation of statistical theory will be limited to only what is needed by the attendees to understand and implement processes and monitoring tools within the statistical framework.
Presented examples will include an emphasis on the manufacturing processes and quality assurance needs of product in the medical device and pharmaceutical industries.
Minitab statistical software will be used to demonstrate data collection and input, and how to build and interpret various process control charts for both attributes and variables data. The seminar will also include the use of Minitab to develop attributes and variables sampling plans for quality assurance and acceptance. A handout and dataset will be provided to attendees so they may work hands-on with the information presented in the seminar.
June 19th, 2019
The power of your study is the probability that you will find a statistically significant difference or relationship (an “effect”) if that difference or relationship (effect) truly exists in the population.
A study with too small of a sample size is under-powered. This means that even if the effect you are testing for truly exists, you won’t achieve statistical significance. You will waste time by collecting a sample that is too small to properly power a study. Why perform a research if you can’t see significance for your desired effect?
A study with too large of a sample is over-powered. This means that you’ve collected such a large sample that you will see significance even on very small effects. However, the costs of subject recruitment, data collection, and follow-up (if needed) are quite large. Recruiting more subjects than needed unnecessarily inflates the temporal and monetary costs.
Questions related to the feasibility of a study can be answered by power analysis:
In this webinar attendees will learn the statistical power analysis and techniques for determining sample size (a priori techniques) calculation. Also attendees will get work examples in the free to use G*Power software. Some code and demonstrations will be provided for powering studies and performing power analysis simulations in R software.
June 26th, 2019
Predicting future outcomes, the next steps in a process, or the best choice(s) from an array of possibilities are all essential needs in many fields. The predictive model is used as a decision making tool in advertising and marketing, meteorology, economics, insurance, health care, engineering, and would probably be useful in your work too!
Join Elaine Eisenbeisz of Omega Statistics as she presents the rationale and risks of predictive modeling via supervised learning techniques. Elaine will also provide an overview of some of the many available modeling techniques including:
• Linear regression
• Logistic regression
• Linear discriminant analysis
• K-Nearest Neighbors
• Resampling methods (Cross-Validation, Bootstrap)
• Subset selection
• Shrinkage methods (Ridge regression, Lasso regression)
• Tree-Based methods (Decision trees, Bagging, Random Forests, Boosting)
July 24th, 2019
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.
Randomized clinical trials remain the standard for clinical research. However, the cost of a traditional randomized controlled trial especially in large sample sizes and long study duration, are limiting factors of innovation in the pharmaceutical and medical device arenas. A trial with an adaptive design can often result in lower costs and more efficiency by making use of interim analyses with data accumulated during the course of a trial to modify a study, without compromising validity and integrity. Adaptive designs also allow for checking trial assumptions and progress before the conclusion of the study. Studies can be adapted for dose response, patient accrual, and early stopping for futility or patient safety concerns.
However, there are special considerations in the planning and execution of a adaptive design to control Type I error rates and ensure consistency of treatment. In additional to protocol considerations, the FDA and regulatory agencies also require particular assurances that an adaptive design will incorporate flexibility at the expense of sacrificing study validity and patient safety.
This webinar includes an overview of Adaptive Design, and an emphasis in group sequential design, sample size re-estimation, and Phase II/III Adaptive Seamless Designs.
The objective of the webinar 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.
R statistical software will be used to demonstrate analyses and simulation studies within the adaptive design framework. So bring your laptop computer!
August 1st and 2nd, Irvine, CA
Clinical data management (CDM) is paramount for a successful research. After all, Garbage In, Garbage Out (GIGO).
CDM involves all aspects of collecting, processing, and interpreting information. There are many types of computer applications and database systems to support data collection and management. However, there are elements of CDM that apply across the board. Review and approval of drugs or devices by regulatory agencies requires the assumption that the data presented are valid and reliable. Integrity of the data is paramount to ensure confidence in the results and conclusions you will make.
This seminar is based on FDA E6 GCP Guidelines which are the basis of effective data quality management. Even if your research is not FDA regulated, the information you learn in this course will help to ensure a robust data collection and management plan. The information conveyed in this course will also assist investigators in setting up processes for smoother data monitoring and auditing.
Examples of CRF’s and required documentation will be presented. Data collection and preparation techniques will also be demonstrated, and will include the use of CDISC and SDTM standards. Additionally, this workshop will provide you with the knowledge and tools needed to assure a CDM plan that holds up when the inevitable deviations from protocol occur.
September 5th and 6th, Boston, MA
Statistics is a useful decision-making tool in the clinical research arena. When working in a field where a p-value can determine the next steps on development of a drug or procedure, it is imperative that decision makers understand the theory and application of statistics.
Many statistical softwares are now available to professionals. However, these softwares were developed for statisticians and can often be daunting to non-statisticians. How do you know if you are pressing the right key, let alone performing the best test?
This seminar provides a non-mathematical introduction to biostatistics and is designed for non-statisticians. And it will benefit professionals who must understand and work with study design and interpretation of findings in a clinical or biotechnology setting.
The focus of the seminar is to give you the information and skills necessary to understand statistical concepts and findings as applies to clinical research, and to confidently convey the information to others.
Emphasis will be placed on the actual statistical (a) concepts, (b) application, and (c) interpretation, and not on mathematical formulas or actual data analysis. A basic understanding of statistics is desired, but not necessary.