Clinical Development Course Descriptions
Kestrel's webinars and courses prepare you to address today's Clinical Research challenges.
All Kestrel educational webinars are first offered live, and then recorded so they may be either downloaded or viewed on-demand for ease and accessibility.
NEW! For a selection of new courses, including Diving into Domains, How to Design Excellent CRFs, and Demystifying the Query Process, and for discounted pricing, please visit our newest campus at TrainingCampus.
Webinars
Kestrel offers two kinds of educational webinars in live broadcasts and on-demand formats. The first is approximately 90 minutes long and the curriculum is mostly based on data quality and clinical data standards topics. This webinar format covers a great deal of information in depth.
Diving into Domain webinars are 45 minute learning modules. These modules are narrower in scope, allowing for a meaningful exploration of each topic in a shorter period of time. For example, several modules are geared toward understanding common issues with specific data domains (e.g., AEs, demographics), including non-technical aspects of CDASH and SDTM.
All of Kestrel’s webinars include a lecture, slide deck and quiz. Many webinars also provide supplemental materials and tools. To register for Kestrel’s educational webinars, please visit the Registration page.
Diving into Domains
Track: Data Domains and Concepts Explained
- Domain Module 1: Adverse Events Part 1
- Domain Module 2: Adverse Events Part 2
- Domain Module 3: Demography & Subject Characteristics
- Domain Module 4: Prior & Concomitant Medications
Standard Webinars
Track: Standards in Clinical Trials
Series: Standards Management 101
A 7-Part Series on Managing Clinical Data Standards- Part 1: An Introduction to Standards
- Part 2: Standards Development
- Part 3: Standardization and Flexibility
- Part 4: Governance
- Part 5: Managing Standards
- Part 6: Standards in Contracted Clinical Development
- Part 7: When Standards Collide
Series: Designing Clinical Data Standard
- Title: Best Practices in Designing Clinical Data, Part 1
- Title: Best Practices in Designing Clinical Data, Part 2
Track: Quality in Clinical Data
Series: Reducing Data Queries in Clinical Trials- Title: A Collaborative Approach to Improving Data Quality
- Title: Using Patterns Data to Look for Bias and Fraud
- Title: Applying Risk Management in Cleaning Clinical Data Part 1: Understanding the Concepts
- Title: Applying Risk Management in Cleaning Clinical Data Part 2: Applying the Model
- Title: Database Audits Today
- Title: Establishing an eClinical Vendor Management Program (Upon Request)
On-Site Education
Every course offered by Kestrel can also be conducted on-site, minimizing travel and down time, and allowing teams to share the training experience at the most convenient time. On-site courses can be customized and include hands-on exercises drawn from the client's own business. A 90 minute follow-up teleconference is included, which can be used for follow-up questions, or additional discussions.
Examples of on-site courses include:
- Standards Management 101: a three day course covering the material included in the 7 part webinar series, plus tips for implementing standards in the client's environment
- Best Practices in Clinical Data Design: a two day course that draws together the material in the two part series, along with an assessment of selected practices at the client site, if desired.
- Quality in Clinical Data: a two day course presenting the material from Applying Risk Assessment in Cleaning Clinical Data and Using Aggregate Data Checks to Look for Bias and Fraud.
COURSE DESCRIPTIONS
Diving into Domains
Track: Data Domains and Concepts
Series: Data Domains and Concepts ExplainedThe following learning modules will benefit:
- Data Management Professionals
- Clinical Operations
- Standards Managers
- EDC Designers and Programmers
- Statisticians
- Clinical Managers
- Clinical Monitors (CRAs)
Domain Module 1: Adverse Events Part 1 NEW
Instructor: Kit Howard, MS, CCDM, CRCP
Description: Adverse events are key data in virtually all clinical trials. Understanding the concepts and their application is critical to ensuring subject safety. This module explains the different types of AEs, the expected variables and how they should be structured. Elements of pre-approval drug and device studies are discussed.
- What are Adverse Events (AEs), Serious Adverse Events (SAEs) and Treatment Emergent Adverse Events)
- What can and cannot be an AE?
- What are significant non-serious AEs? Why are they different?
- Defining Outcome of AE: what is its intent, how is it used, what are the recommended code list values and what do they mean?
- Using Outcome of AE to eliminate the AE Ongoing? field
- How is Relationship to Treatment be used for drugs and devices? What is the official code list? How reliable is the information? Is it really necessary?
- AE Severity short code list vs. long list and when should each be used?
- Different approaches to capturing changing severity, what is really analyzed and what matters and when
Domain Module 2: Adverse Events Part 2 NEW
Instructor: Kit Howard, MS, CCDM, CRCP
Description: This module is a continuation of Adverse Events Part 1 (see above). It covers additional questions that often arise when defining, capturing and using AE data.
- Differentiating Action Taken with Study Treatment vs. Action Other in drugs and devices
- When to start collecting AEs and SAEs? At informed consent? At time of first treatment? What do the regulations say?
- Should AEs be diagnoses or symptoms? Who can decide and the consequences of doing one vs. the other
- AE Seriousness – Should you use one overall yes/no question or ask about each Seriousness criterion? Are there times to ask both? Neither?
- How can you know if an AE occurred pre and post dose on Day 1? Do you have to collect time of AE for all Day 1 AEs, and does it really matter? (Hint: no, it doesn’t)
- How to tell if sites are recording the right AEs and not just those in the IB, those they think are related or those they think are unexpected.
Domain Module 3: Demography & Subject Characteristics NEW
Instructor: Kit Howard, MS, CCDM, CRCP
Description: Demographic data and Subject Characteristics data both describe subjects, but are used differently and there are different expectations for regulatory submissions. This module describes these data types, how to structure them, and many of the challenging issues in their capture and use.
- Defining Race and Ethnicity – what do the regulations actually say? What do Race and Ethnicity really tell us? How should you define race outside of the US, map codes and capture multiple responses.
- Sex and Gender – What are the correct terms and the permitted responses? When should it be captured? How should you handle variations (e.g., transgender) and do they matter?
- Birth date, age and units and how they are used. Given privacy issues, what minimum data must be available and why?
- The purpose of Demography and Subject Characteristics variables
- Capturing and using other SC variables, e.g., gestational age, SES, eye color, subject initials and childbearing potential
- SC variables are to be captured only once but what if you need to capture changes (changes in eye color, childbearing potential?
Domain Module 4: Prior & Concomitant Medications NEW
Instructor: Kit Howard, MS, CCDM, CRCP
Description: Prior and Concomitant Medications provide clues to understanding adverse events, identifying non-serious events of interest, and detecting possible drug-drug interactions. This module discusses aspects of both data capture and reporting, providing insight into how these data are used.- Handling concomitant vs prior medications – different ways to differentiate them, including CDISC flags
- How should you handle medications of special interest?
- Optimizing use of the CDISC indication field
- Is there value in a separate Reason for Use field?
- Medications vs therapies – what is the difference, how should non–medication therapies be handled, and when are they important?
- Recording active ingredients and when it makes a difference
Future Data Domains and Concepts Titles
- Medical History
- Exposure (therapeutic treatment)
- Vital Signs
- Laboratory Data
- Protocol Deviations
- Substance Use
- Disposition (subject status)
- Eligibility Criteria
- Modeling data as vertical/horizontal (or normalized/non-normalized or short and wide/tall and skinny)
- Interpreting the regulations to understand data capture needs
- Protocol review for data managers
- Validating questionnaires - what’s involved
- Interpreting the regulations to understand data capture needs
- Protocol review for data managers
- Validating questionnaires - what’s involved
Track: Standards in Clinical Trials
Series: Standards Management 101.
Instructor: Kit Howard, MS, CCDM, CRCP
Description: Clinical research organizations are realizing that adopting clinical data standards is a matter of "when," not "if," but developing and managing standards require a specialized knowledge base that does not exist in most organizations. This first-of-a-kind webinar series teaches the skills needed for ongoing development, management, maintenance, tracking and retirement of standards in today's increasingly sophisticated and constrained environment. The topics covered include:
This series will benefit:
- Leaders exploring process improvement and standards implementation
- Heads and colleagues in
- Standards management
- Process improvement
- Clinical data management
- EDC design and programming
- Clinical operations
- Clinical monitoring
Series: Designing Clinical Data Standards
Title: Best Practices in Designing Clinical Data, Parts 1 & 2Instructor: Kit Howard, MS, CCDM, CRCP
Description: Structuring clinical data is an art and, while current standards help, they leave many implementation questions unanswered. Using CDISC domains as a starting point, this two part series examines underlying data design assumptions, illustrates the consequences of different implementation decisions, and challenges many commonly held beliefs. Anyone who has ever sat in a study team meeting arguing about the best way to capture a given data point will benefit from this course. Topics include:
- The critical link between data design and process
- What does "Capture only necessary data" mean? (Hint, not what you think it does!)
- Horizontal (denormalized) vs. vertical (normalized) structures and why and when each is used
- Different options for capturing Serious AE data with pros and cons of each
- Modeling questionnaires horizontally vs. vertically and when it matters, e.g.,
- Different numbers of answers for questions in one questionnaire
- Different answers for different questions in one questionnaire
- Dealing with controlled terminology in questionnaires
- Epochs and elements: what they are and what they can for you
- Precision in data capture and why your data may be less precise than you think
- Standards management
- Clinical data management
- Clinical database programming
- Statistical programming
- Anyone involved with modeling clinical data
Series: Data and Regulations
Title: Minimizing the Impact of FDA's Drug Induced Liver Injury Guidance on All Preapproval Clinical Trials (FREE)
Instructor: Kit Howard, MS, CCDM, CRCP
Description: In July 2009, the FDA issued the "Drug Induced Liver Injury Premarketing Clinical Evaluation" guidance that places new data capture and analysis requirements for all clinical trials on unapproved drugs. DILI is defined as severe liver damage potentially induced by the administration of a drug. The guidance describes how to monitor for the condition and additional data that must be captured that could cause a significant additional data collection burden. This webinar reviews key components of the guidance and the additional data required, and shows how to evaluate the risks of different approaches rather than assuming that all data are required for all subjects.
This webinar is a "must attend" event for everyone involved with defining, capturing, analyzing or reporting on clinical data or monitoring subject safety for unapproved drugs in both industry and academia.
Who Should Attend:- Clinical Site Personnel
- Clinical Investigators
- Clinical Study Coordinators
- Clinical Research
- Clinical Operations
- Clinical Data Management
- Biostatistics
- Clinical Communications
- Clinical Data Standards
- Regulatory Affairs / Regulatory Compliance
- Clinical Quality Assurance
- Drug Development Management
Series: Topics in Clinical Standards
Title: An Overview of Current Standards and How They Can Help You (FREE)
Instructor: Kit Howard, MS, CCDM, CRCP
Description: There are myriad standards available for defining safety and efficacy clinical trials data, both inside and outside of the industry. Some are obvious, such as CDISC, but some are less well known. Some will be required, and others can provide already-developed materials, thus saving time and money for the company. This webinar:
- Examines the definition of "standards"
- Reviews a wide range of standards relevant to clinical research, including initiatives at NIH, the Joint Initiative Council, ISO standards, and electronic health record initiatives
- Provides implementation suggestions
- Standards management
- Process improvement
- Clinical data management
- EDC design and programming
- CRF design
- Database programming
- Biostatistics
- Clinical science/study management
- Clinical Operations
Track: Quality in Clinical Data
Series: Reducing Data Queries in Clinical Trials
Title: A Collaborative Approach to Understanding Data Quality
Instructor: Kit Howard, MS, CCDM, CRCP
Description: Some believe that the number of data queries in a study reflects the quality of the data - the more queries, the worse the quality. It really measures how well the investigative site followed the sponsor's data quality rules, but sites rarely know or understand those rules. In fact, few sponsors or CROs agree internally on what "data quality" means. This webinar:
- Defines "quality" in alignment with the Institutes of Medicine reports
- Demonstrates why our current approach to data cleaning sets the sites up for failure
- Presents a practical method for dramatically reducing data queries by ensuring a cross-functional understanding of study-specific "data quality" definitions
- Describes a method for ensuring that all quality requirements are defined in common language, not "computerese"
- Provides additional tips for helping the sites to generate high quality data
- Standards management
- Process improvement
- Clinical data management
- EDC design and programming
- CRF design
- Database programming
- Biostatistics
- Clinical science/study management
- Clinical Operations
- Clinical monitoring
- Investigator site coordination (CRCs)
Series: Reducing Data Queries in Clinical Trials
Title: Using Data Patterns to Look for Bias and Fraud
Instructor: Kit Howard, MS, CCDM, CRCP
Description: There is evidence that much of current data cleaning does little to increase data quality, in the sense that data being "clean" or "dirty" rarely change the outcome of the study. Worse, it usually misses bias and fraud, which are a much greater risk to the trial conclusions. This webinar:
- Examines different methods for assessing data quality during the study
- Includes methods for
- looking for patterns in aggregated data rather than individual data points
- comparing data across subjects, sites and time
- Describes how to interpret the results when unexpected patterns emerge
- Reviews a selection of case histories
- Targets non-statisticians who wish to implement tools that do not require specialized software or statistical knowledge
- Standards managers
- Clinical data managers
- EDC designers and programmers
- Statisticians
- Clinical monitors (CRAs)
Series: Risk Management in Clinical Trials Data Quality
Title: Applying Risk Assessment in Cleaning Clinical Data: Part 1: Understanding the Concept
Title: Applying Risk Assessment in Cleaning Clinical Data:Part 2: Applying the Model
Instructor: Kit Howard, MS, CCDM, CRCP
Description: The FDA now recognizes delivering "perfect" data is neither cost-effective nor feasible, and is embracing a risk-based approach to data quality. This two part series challenges the idea that all data should be cleaned equally. It explores the question of "risk to what/whom" and what "quality" means in this context. The first webinar covers the conceptual material and the second presents the practical application. Based in part on work by the Data Quality Research Institute, this two part webinar:
- Identifies a two-tiered approach to risk management in data quality
- A decision model for deciding what types of errors are low, medium or high risk
- A mechanism for applying those decisions systematically and objectively to specific data points
- Demonstrates how to use the model when the same domain is implemented in different ways (e.g., blood pressure as safety vs. efficacy)
- Suggests a documentation approach for making the strategy transparent internally and to the regulatory agencies
The materials include algorithms for defining critical vs non-critical data, and decision trees for categorizing data domains as primary, secondary and tertiary. These categorizations drive the data cleaning to be done.
This webinar will benefit those who define organizational strategy and implementation for clinical data quality, including heads and colleagues in:
- Standards management
- Process improvement
- Clinical data management
- EDC design and programming
- Database programming
- Biostatistics
- Clinical science/study management
- Clinical Operations
- Clinical monitoring
Series: Topics in Data Quality
Instructor: Kit Howard, MS, CCDM, CRCP
Description: Database audits have been a staple of the clinical data manager's quality arsenal for decades, and they persist even in the face of persistent nagging questions about their validity and usefulness. These questions are even more relevant now that adoption of EDC, ePRO and other electronic data capture media is accelerating. This webinar examines the traditional database audit and demonstrates why it did not answer the question we thought it was asking. It also suggests a better approach for paper-based studies and an equivalent process for studies where there is no "CRF", such as EDC. The content is based upon work done by the Data Quality Research Institute.
This webinar will benefit heads and colleagues in:- Standards management
- Process improvement
- Clinical data management
- EDC design and programming
- Clinical operations
- Clinical monitoring
Presenter: Jonathan Andrus, M.S., CQA, CCDM.
Description: This course will provide the participant with information that they can use to establish a vendor management program to effectively manage and qualify their eClinical vendors. As organizations outsource more and more often, it is becoming more important than ever to have a well-defined vendor qualification and management program. The course will include:
- Identify the essential components of an effective vendor management/qualification program
- Understand the content and usage of the quality agreement with vendors
- Determine which vendors need to be qualified and how to manage vendors across different geographical locations
- Understand what to be prepared to demonstrate to FDA with regard to vendor qualification
- How to conduct audits and maintain the necessary documentation
- What to include in a vendor qualification/management procedure
- How to manage vendor change (software, study, IT, etc)
Case studies and exercises bring the course materials to life and allow the participant to apply information covered during the course. Webinar materials include examples of a paper audit checklist, on-site audit checklist, pre-audit questionnaire, and audit request form.
This webinar will benefit:- Data Management Professionals
- Quality Assurance Professionals
- Clinical Managers
- Purchasing
- Outsourcing Managers
- eClinical Vendors