- Statistical Elements of Implementing ICH Quality Guidelines
This seminar explores the unique challenges facing quality functions of pharmaceutical and biotechnology companies. Attendees will learn practical implementation solutions as well as best practice descriptions that will allow management to effectively assess, manage and mitigate risk of poorly designed studies. Participants will learn statistical methods related to ICH guidelines and will discover how regulatory agencies, such as the FDA expect organizations to meet these guidelines. View Details ....
- Technical Writing for Pharma, Biotech and Medical Devices
In this virtual seminar attendees will learn Information required in regulatory submissions, eCTD format and style, The fundamentals of effective writing: accuracy, brevity and clarity, Common mistakes in written English, Effective use of figures and tables, Correct methods of citing literature sources in technical documents, Types of data distribution, Statistical treatment of experimental data, Design of Experiments (DoE), Writing effective procedures View Details ....
- Applied Time Series Analysis in Healthcare
This 4-hour webinar will provide attendees with the theory and application of time series analysis. The main focus will be on auto regressive integrated moving average (ARIMA) techniques. Variations of the ARIMA and other models which operate under non-linear data, non-stationary data, seasonality, and trends will also be examined. View Details ....
- Statistical Elements of Sample Size Calculations for Non-Clinical Verification and Validation Studies
This webinar provides the logic and processes for determining samples sizes for common tests used in verification or validation of processes. The focus of this webinar is on providing the information needed for attendees to know the appropriate measures and formulas to use for the determining sample size and providing justification for the planned sample sizes. View Details ....
- Hypothesis Testing, P-values and Inference: When Thinking like a Statistician Makes Sense
This clinical research webinar will explore the reasoning that formulates null hypotheses and turns researchers’ hair gray. Attendees will learn the why and how of the scientific method and how to view the world with a statistician’s eyes. Also attendees will learn the possibilities and limitations of research questions and hypothesis development, how to interpret statistical findings with p-values, effect sizes, and confidence intervals. View Details ....
- Analytical Method Validation and Transfer
This course will provide a thorough review of regulatory guidelines on method validation and transfer. It provides guidance on how to perform QC analytical test method validations and transfers. View Details ....
- Optimizing Target Weights for Foods and Beverages
This training program will elaborate factors affecting the target weight decision and help determine the tolerable risks of under-filling and the costs of over-filling. Attendees will gain an understanding of process stability and process capability concepts and methods for process optimization. View Details ....
- P&PC, SPC/6Sigma, Failure Investigation, Root Cause Analysis, PDCA, DMAIC, A3
This webinar is intended to provide guidance regarding the CGMPs on manufacturing methods utilizing the US FDA Production and Process Controls for Drugs and Devices and Statistical Process Controls (SPC) as taught by Drs. Demming and others and required also in the CGMPs and under control of variation process guidance. View Details ....
- Statistical Methods for Quality Improvement
This webinar presents an overview of essential quantitative methods for assessing and ensuring product quality. The methods include: Statistical Process Control, Process Capability Assessment, Regression Modeling, Design of Experiments, Hypothesis Testing, and Measurement Systems Assessment. View Details ....
- Good Laboratory Practices (GLPs) - Comparing and Contrasting with Good Manufacturing Practices (GMPs)
The objective of this webinar is to compare and contrast between Good Laboratory Practices and Good Manufacturing practices. In this webinar training attendees will learn What are Good Laboratory Practices, Why were they created, What is the objective of GLPs and how are they associated with GMPs and SOPs, Statistical procedures for data evaluation, Instrumentation validation, Analytical and laboratory certification, Documentation and maintenance of records, Consequences of noncompliance, Disqualification and reinstatement View Details ....
- Sample Size Determination for Design Validation Activities
Statistical Methods are typically used to ensure that product performance, quality, and reliability requirements are met during the Design Validation phase of product development. This webinar discusses common elements of sample size determination and several specific sample size applications for various design validation activities including Reliability Demonstration/Estimation, Acceptance Sampling, and Hypothesis Testing. View Details ....
- Acceptance Sampling Plans for Process Validation and Production Lot Monitoring
This webinar provides details regarding the generation of acceptance sampling plans often used in process validation and production control to ensure quality of final products. By attending this webinar, participants will be able understand the key inputs and issues involved in determining acceptance sampling plans. Sampling plans for attribute data are the primary focus although variable acceptance sampling plans are presented as well. View Details ....
- Useful Statistical Methods for Defining Product and Process Specifications - Part I
This webinar covers useful and important statistical methods that assist scientists and engineers in the development of appropriate product and process specifications. Appropriate product specifications are critical to achieving adequate and reliable product performance. View Details ....
- Useful Statistical Methods for Defining Product and Process Specifications - Part II
This webinar covers useful and important statistical methods that assist scientists and engineers in the development of appropriate product and process specifications. Appropriate product specifications are critical to achieving adequate and reliable product performance. View Details ....
- Validation Sampling Plans
This webinar will discuss setting up statistically justified sampling plans for process validation. Discussion will also involve using the sampling plan to set acceptance criteria for process validation. Setting acceptance criteria for test method validation will also be presented. View Details ....
- Introduction to Medical Device Quality System Regulations
In this webinar training, you will get an overview of FDA’s medical device Quality System Regulation, 21 CFR Part 820. Attendees will learn Regulatory basis, Quality System, Design Controls, Document Controls, Purchasing Controls, Identification & Traceability, Production & Process Controls, Acceptance Activities, Non-conforming Product, Corrective & Preventive Action, Labelling & Packaging Control, Handling, Storage, Distribution & Installation, Records, Servicing, Statistical Techniques View Details ....
- Solving Statistical Mysteries - What Does FDA Want?
This webinar provides some practical and useful answers to the question "What Kind of Statistical Methods and Tools Does the FDA Want Pharma to Use?". This presentation provides an overview of what it appears the FDA is looking for in the use statistics including examples and recommended approaches. View Details ....
- How to Detect Lack of Data Integrity
This webinar provides some practical and useful answers to the question: “How to Detect Lack of Data Integrity?” Humans, equipment or both can be the source of lack of data integrity. This session discusses both types of data integrity sources and introduces the assessment of “data pedigree” as a concept that puts focus on the types of data integrity issues and analytical and statistical methods for detecting data problems. Pharma and biotech case studies are used throughout the presentation to illustrate how the various approaches fit together. View Details ....
In today’s clinical research and biotechnology environments, decisions are driven by data. A single p-value can determine whether a promising treatment moves forward — or is left behind. For many professionals, however, statistics can feel like a foreign language. Complex software, intimidating formulas, and confusing terminology often stand in the way of confidently interpreting results.
This seminar bridges that gap. Designed specifically for non-statisticians, it provides a clear, practical introduction to biostatistics without overwhelming you with mathematics. You’ll learn the “why” behind the numbers, gain tools to spot meaningful findings, and avoid being misled by misused statistics. By the end of the program, you’ll be able to read statistical reports with confidence, communicate findings to colleagues and stakeholders, and make better data-driven decisions that can shape the future of clinical and biotech innovation.
Why Should You Attend:
In the world of clinical research and biotechnology, numbers carry weight. A single statistical finding can influence whether a project moves forward, a regulatory submission is approved, or a study is deemed credible. Yet for many professionals, statistics can feel like a barrier rather than a tool. Questions often linger: Was the right test used? Is this “significant” result actually meaningful? Am I interpreting this report correctly?
These uncertainties can create hesitation and reduce confidence in decision-making. While statisticians may handle the technical details, non-statisticians are frequently expected to review reports, contribute to study design discussions, and communicate findings to colleagues, regulators, or clients. Without a clear understanding of statistical concepts, it can be easy to misinterpret results or miss important nuances.
This seminar is designed to help bridge that gap. In two focused days, you’ll be introduced to the key ideas and methods that appear most often in clinical and biotech research. The emphasis is on interpretation and application, not heavy mathematics. You’ll see how statistical results fit into the bigger picture of study design, regulatory requirements, and scientific communication.
By attending, you will:
- Develop a clearer understanding of common statistical terms and concepts.
- Gain practical insight into how and why certain tests are applied.
- Learn approaches for distinguishing between statistically significant and clinically meaningful results.
- Improve your ability to follow discussions with statisticians and regulatory professionals.
- Increase your confidence when reviewing, reporting, or presenting statistical information.
This seminar is not about turning you into a statistician. Rather, it is about equipping you with enough knowledge to understand the essentials, ask informed questions, and engage more effectively in the decision-making process. For professionals who work with clinical data but don’t have formal statistical training, this program provides a practical and supportive way to reduce uncertainty and build confidence.
Who will Benefit:
- Physicians
- Clinical Research Associates
- Clinical Project Managers/Leaders
- Sponsors
- Regulatory Professionals who use statistical concepts/terminology in reporting
- Medical Writers who need to interpret statistical reports
- Clinical Investigators / Principal Investigators (PIs)
- Medical Affairs Professionals
- Safety and Pharmacovigilance Staff
- Data Managers and Data Analysts in clinical trials
- Healthcare Policy Analysts
- Pharmaceutical & Biotechnology Executives and Managers
- Graduate Students in the biological and medical sciences
- Anyone interested in learning core concepts and application of statistics
In-Person Seminar going Virtual with increased learner satisfaction.
Yes, attend this seminar from anywhere. We are making it real and more interactive – Here's a sneak peek:Our enhanced delivery process and technology provides you an immersive experience and will allow you to access:
- The real-time and live presentation as in in-person events
- Private chat for company-specific conversation – the same as you would get in an in-person seminar
- Opportunities to connect with your peers to share knowledge at a different time and have group discussions
- Live workshop activities
- Live Q&A during the event and offline Q&A assistance after the event
- As usual more content, activities and case studies and now adding homework for a comprehensive understanding
- Certification
Topic Description:
Statistics can be described as the science of making decisions in the face of uncertainty. Nowhere is this more important than in clinical research and biotechnology, where the conclusions drawn from data can determine whether a new treatment advances, a regulatory application succeeds, or a scientific paper influences practice. For professionals without formal training in statistics, the numbers, formulas, and jargon can feel daunting. Yet, the ability to understand and communicate statistical findings is no longer optional — it is essential.
This two-day seminar, Biostatistics for the Non-Statistician, was created to bridge the gap between statistical theory and practical application. Its goal is not to turn attendees into statisticians, but to provide enough knowledge to confidently interpret results, recognize when statistics are being misapplied, and communicate findings with clarity. Across lectures, discussions, and exercises, the program emphasizes understanding, application, and interpretation, not memorization of formulas.
Day 1 introduces participants to the fundamental building blocks of statistical reasoning. The day begins with an exploration of why statistics matter in research and healthcare decision-making. Participants will examine the essential distinction between samples and populations, learn why variability and uncertainty must be accounted for, and gain insight into what statistics can — and cannot — accomplish. By dispelling the myth of the statistician as a “magician,” this session underscores the value of clear, transparent methods over mysterious or opaque analyses.
The day continues with an in-depth look at the many ways data can be interpreted. Key concepts such as p-values, effect sizes, and confidence intervals are explained in plain language, along with the differences between statistical and clinical significance. By demystifying these measures, attendees will be better equipped to recognize whether findings are not only statistically valid but also practically meaningful in real-world contexts.
Attention then turns to the application of common statistical tests. Participants will learn why testing is necessary through the lens of Null Hypothesis Significance Testing (NHST), and will gain familiarity with comparative tests, correlation methods, multiple regression analysis, and non-parametric techniques. Rather than teaching calculations, this session emphasizes understanding when and why each method is used, along with the strengths and limitations of each approach. The day concludes with an introduction to Bayesian logic, which offers a different perspective on statistical thinking. Attendees will see how Bayesian methods can provide richer insights into diagnostics, genetics, and other areas where uncertainty plays a central role.
Day 2 builds on this foundation of Day 1 with applied and forward-looking topics. The day begins with a guided exercise in interpreting systematic reviews, a cornerstone of evidence-based medicine. Participants will learn how to evaluate statistical language across multiple studies, identify sources of bias, and judge the transparency and reproducibility of reported findings. Emphasis is placed on developing the ability to communicate insights clearly to both technical and non-technical audiences.
The next session explores study power and sample size — critical considerations in trial design and regulatory submissions. Attendees will review how concepts such as p-values, effect size, and significance levels come together in sample size calculations, and will be introduced to available formulas, software tools, and practical resources for applying these concepts in real projects.
Then, participants will learn the essential steps in developing a Statistical Analysis Plan (SAP). Using FDA guidance as a foundation, the session walks through how to design a plan that ensures clarity, compliance, and rigor. A template SAP is provided to give attendees a concrete starting point for their own work.
The seminar closes with a discussion of specialized topics and an open Q&A. Here, participants will explore survival analysis methods, applications in pharmacokinetics and pharmacodynamics, and strategies for taking a holistic view of study design and interpretation. This final session ties together the threads of the seminar, encouraging participants to approach data with both confidence and critical thinking.
By the end of the two days, attendees will not only understand the language and logic of biostatistics but will also be able to apply these concepts to their daily work. They will leave better prepared to evaluate published research, collaborate with statisticians, develop study plans, and communicate statistical findings clearly to colleagues, regulators, and other stakeholders. Most importantly, they will gain the assurance that statistics are not a barrier, but rather a powerful tool to improve decision-making and advance clinical and scientific discovery.
- Session 1 (60 Mins): Why Statistics?
- Do we really need statistical tests?
- Sample vs. Population
- I’m a statistician not a magician! What statistics can and can’t do
- Descriptive statistics and measures of variability
- Session 2 (60 Mins): The many ways of interpretation
- Confidence intervals
- p-values
- effect sizes
- Clinical vs. meaningful significance
- Session 3 (90 Mins): Common Statistical Tests
- Comparative tests
- Correlation and Regression analysis
- Session 4 (60 Mins): Bayesian Logic
- A different way of thinking
- Bayesian methods and statistical significance
- Bayesian applications to diagnostics testing
- Bayesian applications to genetics
- Session 1 (60 Mins): Systematic Reviews
- Why perform systematic reviews and meta-analysis?
- A bit of history, reasoning, and terminology
- Steps in performing a systematic review
- Session 2 (60 Mins): Study power and sample size
- Review of p-value, significance level, effect size
- Formulas, software, and other resources for computing a sample size
- Session 3 (60 Mins): Developing a Statistical Analysis Plan
- Using FDA guidance as a foundation, learn the steps and criteria needed to develop a statistical analysis plan (SAP).
- An SAP template will be given to all attendees.
- Session 4 (90 Mins): Specialized topics/Closing Comments/Q&A
- Survival Curves and Cox regression
- Tests of Equivalence and Non-inferiority
- Question and Answer session
Elaine Eisenbeisz
Owner, Omega Statistics
Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a consulting firm she founded in 2006 to provide statistical design, analysis, and consulting services across a range of diverse fields. Over her career, Elaine has partnered with private researchers, biotech start-ups, and established companies such as Allergan, Nutrisystem, and Rio Tinto Minerals. She has also collaborated on peer-reviewed publications with academic and industry colleagues, bringing practical and methodological expertise to diverse projects.
Elaine earned her B.S. in Statistics from the University of California, Riverside, and a Master’s Certification in Applied Statistics from Texas A&M University. She is a member of the American Statistical Association and the Mensa High IQ Society, and her company, Omega Statistics, maintains an A+ rating with the Better Business Bureau.
Her professional interests include Real-World Data and Evidence (RWD/RWE), genetics, proteomics, liquid biopsies, and fragmentomics. Beyond her consulting work, Elaine is passionate about education and regularly presents seminars, webinars, and workshops designed to make statistical concepts clear and accessible to non-statisticians. Known for her engaging teaching style and ability to translate complex ideas into practical applications, she has helped countless professionals gain confidence in applying statistics to their work.
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