Teams involved in clinical trials include the clinical trials team, statistician team, study set up team, and data management team. These teams perform various functions like conducting trials, generating reports for regulatory authorities, developing electronic case report forms, and managing data and discrepancies.
There are typically four phases in a clinical trial process: planning, implementation, analysis, and reporting. Clinical trials can be open label, single blinded, or double blinded depending on whether the patient and/or doctors are aware of the treatment given.
Clinical Data Management Plan_Katalyst HLSKatalyst HLS
Introduction to Data Management Plan in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Electronic Data Capture & Remote Data CaptureCRB Tech
CRB Tech is one of the best leading Software Development Company in Pune. We are offering Software Development Services as well as IT Training including Java, Dot Net, SEO and Clinical Research training in pune.
Clinical Data Management (CDM) is a critical component of clinical research that involves the collection, cleaning, validation, and management of clinical trial data to ensure its accuracy, integrity, and compliance with regulatory requirements. The workflow of CDM typically consists of several key stages, each with specific activities and processes. Here is an overview of the typical workflow of CDM:
Study Startup:
Protocol Review: CDM teams begin by reviewing the clinical trial protocol to understand the study's objectives, endpoints, data collection requirements, and timelines.
Database Design: Based on the protocol, the team designs a data capture system or electronic data capture (EDC) system. This includes creating data entry forms, defining data validation checks, and setting up data dictionaries.
Data Collection:
Case Report Form (CRF) Design: CDM professionals design electronic or paper CRFs to collect data during the trial. CRFs capture specific data points required by the protocol.
Data Entry: Data is entered into the CRFs, either electronically by site personnel or through paper CRFs.
Data Validation: CDM teams implement validation checks to ensure data quality and consistency. Data validation checks may include range checks, consistency checks, and logic checks.
Query Management: Queries are generated when data discrepancies or inconsistencies are identified. CDM teams send queries to investigational sites for resolution.
Data Cleaning and Quality Control:
Data Cleaning: Data are cleaned to resolve discrepancies, discrepancies, and inconsistencies. This involves querying data discrepancies with clinical trial sites.
Data Review: CDM teams review data to ensure completeness and accuracy, and any outstanding queries are resolved.
Quality Control: Quality control processes are applied to verify the integrity and accuracy of data.
Database Lock:
Once the data are cleaned, reviewed, and validated, the database is locked, indicating that no further changes can be made to the data. Database lock is a critical step before data analysis begins.
Data Export and Analysis:
Data is exported from the database and provided to biostatisticians and researchers for statistical analysis. This analysis is conducted to determine the study's outcomes, efficacy, and safety profile.
Data listings, summaries, and tables are generated for regulatory submissions, reports, and publications.
Final Study Reporting:
After data analysis, CDM teams contribute to the preparation of final study reports, which provide a comprehensive overview of the trial's results, data quality, and regulatory compliance.
Archiving and Documentation:
Clinical trial data, documentation, and databases are archived to ensure their long-term availability for regulatory audits and future reference.
Regulatory Submission: CDM teams provide support for regulatory submissions.
Clinical Data Management Plan_Katalyst HLSKatalyst HLS
Introduction to Data Management Plan in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Electronic Data Capture & Remote Data CaptureCRB Tech
CRB Tech is one of the best leading Software Development Company in Pune. We are offering Software Development Services as well as IT Training including Java, Dot Net, SEO and Clinical Research training in pune.
Clinical Data Management (CDM) is a critical component of clinical research that involves the collection, cleaning, validation, and management of clinical trial data to ensure its accuracy, integrity, and compliance with regulatory requirements. The workflow of CDM typically consists of several key stages, each with specific activities and processes. Here is an overview of the typical workflow of CDM:
Study Startup:
Protocol Review: CDM teams begin by reviewing the clinical trial protocol to understand the study's objectives, endpoints, data collection requirements, and timelines.
Database Design: Based on the protocol, the team designs a data capture system or electronic data capture (EDC) system. This includes creating data entry forms, defining data validation checks, and setting up data dictionaries.
Data Collection:
Case Report Form (CRF) Design: CDM professionals design electronic or paper CRFs to collect data during the trial. CRFs capture specific data points required by the protocol.
Data Entry: Data is entered into the CRFs, either electronically by site personnel or through paper CRFs.
Data Validation: CDM teams implement validation checks to ensure data quality and consistency. Data validation checks may include range checks, consistency checks, and logic checks.
Query Management: Queries are generated when data discrepancies or inconsistencies are identified. CDM teams send queries to investigational sites for resolution.
Data Cleaning and Quality Control:
Data Cleaning: Data are cleaned to resolve discrepancies, discrepancies, and inconsistencies. This involves querying data discrepancies with clinical trial sites.
Data Review: CDM teams review data to ensure completeness and accuracy, and any outstanding queries are resolved.
Quality Control: Quality control processes are applied to verify the integrity and accuracy of data.
Database Lock:
Once the data are cleaned, reviewed, and validated, the database is locked, indicating that no further changes can be made to the data. Database lock is a critical step before data analysis begins.
Data Export and Analysis:
Data is exported from the database and provided to biostatisticians and researchers for statistical analysis. This analysis is conducted to determine the study's outcomes, efficacy, and safety profile.
Data listings, summaries, and tables are generated for regulatory submissions, reports, and publications.
Final Study Reporting:
After data analysis, CDM teams contribute to the preparation of final study reports, which provide a comprehensive overview of the trial's results, data quality, and regulatory compliance.
Archiving and Documentation:
Clinical trial data, documentation, and databases are archived to ensure their long-term availability for regulatory audits and future reference.
Regulatory Submission: CDM teams provide support for regulatory submissions.
Clinical Data Management Interview Question Part 2ClinosolIndia
Embarking on a career in Clinical Data Management requires a thorough understanding of the intricacies involved in handling and processing clinical trial data. In this presentation, we explore key interview questions that shed light on the critical aspects of CDM, helping both aspiring professionals and seasoned experts stay abreast of industry trends and expectations.
Have full fleged clinical trial data management systems which bring them a good amount of business and revenue.
CDM is a fundamental process which controls data accuracy of each trial besides helping the timelessness to be achieved.
It helps in linking clinical research co-ordinator = who monitor all the sites & collects the data.
it Links with biostatisticians = who analyze, interpret and report data in clinically meaningful way.
Clinical data management (CDM) is a covered part in the clinical trial and most commonly used tools for the purpose of effectivity of clinical research
SDTM (Study Data Tabulation Model) defines a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting of human clinical trial data tabulations and for non-clinical study data tabulations which are to be submitted as part of a product application(IND and NDA) to a regulatory authority such as the United States Food and Drug Administration (FDA) and PMDA (Japan)
clinical data management in clinical research, helpful for pharmacy, nursing, medical, health care providers, clinical research organization, PharmD, CROs, Clinical trial industry, human biomedical research.
Visit:www.acriindia.com
ACRI is a leading Clinical data management training Institute in Bangalore India.
ACRI creates a value add for every degree. Our PGDCRCDM course is approved by the Mysore University. Graduates and Post Graduates and even PhDs have trained with us and got enviable positions in the Clinical Research Industry. ACRI supplements University training with Industry based training, coupled with hands-on internships and projects based on real case studies. The ACRI brand gives the individual the confidence and expertise to join the ever-growing workforce both in the country and abroad.
Clinical Data Management Interview Question Part 2ClinosolIndia
Embarking on a career in Clinical Data Management requires a thorough understanding of the intricacies involved in handling and processing clinical trial data. In this presentation, we explore key interview questions that shed light on the critical aspects of CDM, helping both aspiring professionals and seasoned experts stay abreast of industry trends and expectations.
Have full fleged clinical trial data management systems which bring them a good amount of business and revenue.
CDM is a fundamental process which controls data accuracy of each trial besides helping the timelessness to be achieved.
It helps in linking clinical research co-ordinator = who monitor all the sites & collects the data.
it Links with biostatisticians = who analyze, interpret and report data in clinically meaningful way.
Clinical data management (CDM) is a covered part in the clinical trial and most commonly used tools for the purpose of effectivity of clinical research
SDTM (Study Data Tabulation Model) defines a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting of human clinical trial data tabulations and for non-clinical study data tabulations which are to be submitted as part of a product application(IND and NDA) to a regulatory authority such as the United States Food and Drug Administration (FDA) and PMDA (Japan)
clinical data management in clinical research, helpful for pharmacy, nursing, medical, health care providers, clinical research organization, PharmD, CROs, Clinical trial industry, human biomedical research.
Visit:www.acriindia.com
ACRI is a leading Clinical data management training Institute in Bangalore India.
ACRI creates a value add for every degree. Our PGDCRCDM course is approved by the Mysore University. Graduates and Post Graduates and even PhDs have trained with us and got enviable positions in the Clinical Research Industry. ACRI supplements University training with Industry based training, coupled with hands-on internships and projects based on real case studies. The ACRI brand gives the individual the confidence and expertise to join the ever-growing workforce both in the country and abroad.
Best practices for implementing and maintaining successful standardsVeeva Systems
Watch the video here: https://bit.ly/3uvar1u
This webinar provides best practices, check-lists and case studies for leveraging standards in clinical trials. From creation and implementation, to governance tools (both internal and with external partners), attendees walk away with actionable insights to leverage with their own organization.
* Understand what to standardize
* Learn several approaches to standards development and when they make sense
* Ensure alignment with key stakeholders
* Maintain and govern standards over time
* Reduce overall configuration time
Who Will Benefit:
* Clinical Data (manager/director/head of) Clinical ops
* Data management
* Biostatistics
* Data science
* Clinical science
* EDC
* Biometrics
* eClinical
* Data standards
* Quantitative sciences
* Informatics
* Data monitoring
* Clinical leads
* Study managers
* Clinical study
* Data manager
* CRA
* CDISC
Meet Your Presenters:
Carla Reis
Director, Client Services, 4G Clinical
Carla Reis, Director of Client Services at 4G Clinical, has over 18 years of experience as an operational leader in developing and implementing RTSM systems in a global pharmaceutical company. Carla was a leader in her organization in establishing vendor management standards and processes. She has helped lead major RTSM process improvement initiatives where she established new and innovated approaches to drug assignment verification and vendor integrations. Carla has presented at industry conferences as a subject matter expert on best practices using RTSM solutions for complex strategies in supply chain management. Carla holds a BS in Neurobiology and Physiology from the University of Connecticut and a certification as Lean Six Sigma Yellow Belt. Carla also holds a Masters in Science in Health Administration with a concentration in Health Informatics from Saint Joseph's University.
Paul MacDonald
Senior Director, Strategy Vault CDMS, Veeva Systems
Paul is Senior Director Vault CDMS, responsible for strategy and direction in data management. With 25+ years experience working in life science at pharma, CRO and technology organisations, Paul brings a strong operational focus in relation to eClinical technology for data management and clinical operations that stretches from EDC, through CTMS to risk based monitoring.
Completing the Data Equation: Test Data + Data Validation = SuccessRTTS
Completing the Data Equation
In this presentation, we tackle 2 major challenges to assuring your data quality:
1) Test Data Generation
2) Data Validation
We illustrate how GenRocket and QuerySurge, used in conjunction, can solve these challenges. Also see how they can be easily integrated into your Continuous Integration/Continuous Delivery pipeline.
Session Overview
- Primary challenges organizations are facing with their data projects
- Key success factors for data validation & testing
- How to setup a workflow around test data generation and data validation using GenRocket & QuerySurge
- How to automate this workflow in your CI/CD DataOps pipeline
to see the video, go to https://www.youtube.com/embed/Zy25i74l-qo?autoplay=1&showinfo=0
Creating a Data validation and Testing StrategyRTTS
Creating A Data Validation & Testing Strategy
Are you struggling with formulating a strategy for how to validate the massive amount of data continuously entering your data warehouse or data lake?
We can help you!
Learn how RTTS’ Data Validation Assessment provides:
- an evaluation of your current data validation process
- recommendations on how to improve your process and
- a proposal for successful implementation
This slide deck addresses the following issues:
- How do I find out if I have bad data?
- How do I ensure I am testing the proper data permutations?
- How much of my data needs to be validated and automated?
- Which critical data endpoints need to be tested?
- How do I test data in my cloud environments?
And much more!
For more information, visit:
https://www.rttsweb.com/services/solutions/data-validation-assessment
The Core of Testing – Dynamic Testing Process – According to ISO 29119 with...TEST Huddle
Testers like to test! But sometimes it is difficult to get to grips with what testing really is; and it can be an overwhelming experience to be handed over a system and asked to test it. Where should you start? What should you test? How should you test it? In this webinar we will go through the dynamic testing process (as defined in ISO 29119 Standard for Software Testing) step by step. This will give an in-depth understanding of what the dynamic testing activities: Test Design and Implementation, Test Environment Set-up, and Test Execution, consist of. You can choose to perform the activities formally or informally, but no matter how you do it, understanding the activities and their purpose will enhance the quality of your testing.
FlorenceAI: Reinventing Data Science at HumanaDatabricks
Humana strives to help the communities we serve and our individual members achieve their best health – no small task in the past year! We had the opportunity to rethink our existing operations and reimagine what a collaborative ML platform for hundreds of data scientists might look like. The primary goal of our ML Platform, named FlorenceAI, is to automate and accelerate the delivery lifecycle of data science solutions at scale. In this presentation, we will walk through an end-to-end example of how to build a model at scale on FlorenceAI and deploy it to production. Tools highlighted include Azure Databricks, MLFlow, AppInsights, and Azure Data Factory.
We will employ slides, notebooks and code snippets covering problem framing and design, initial feature selection, model design and experimentation, and a framework of centralized production code to streamline implementation. Hundreds of data scientists now use our feature store that has tens of thousands of features refreshed in daily and monthly cadences across several years of historical data. We already have dozens of models in production and also daily provide fresh insights for our Enterprise Clinical Operating Model. Each day, billions of rows of data are generated to give us timely information.
We already have examples of teams operating orders of magnitude faster and at a scale not within reach using fixed on-premise resources. Given rapid adoption from a dozen pilot users to over 100 MAU in the first 5 months, we will also share some anecodotes about key early wins created by the platform. We want FlorenceAI to enable Humana’s data scientists to focus their efforts where they add the most value so we can continue to deliver high-quality solutions that remain fresh, relevant and fair in an ever changing world.
Introduction to Aggregate Reporting in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Overview of Validation in Pharma_Katalyst HLSKatalyst HLS
Introduction to Validation Concepts in Pharma, Bio-Pharma, Medical Device, Cosmetics, Food, Beverages industry.
Contact:
Katalyst Healthcare’s & Life Sciences
South Plainfield, NJ, USA 07080.
E-Mail: info@KatalystHLS.com
Study setup_Clinical Data Management_Katalyst HLSKatalyst HLS
Introduction to Study Setup in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Introduction to Aggregate Reporting in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
All about Clinical Trials_Katalyst HLSKatalyst HLS
Introduction to All about Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
Follow us on: Pinterest
Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
2. Teams involved In clinical trail
process
Clinical Trails Team
CTT team perform the trials on different
drugs and send the report to the
regulatory bodies.
Statistician Team
Generates reports which are to be sent to
the drug regulatory authorities of
respective country (Ex: sent to FDA in USA)
Study Set Up Team
Develops the electronic Case Report
Forms(e-CRF) and validations(edit checks)
for a specific study
Data Management Team
Provide the specifications for developing
the Case Report Forms(CRF) and
validations(edit checks) and does
discrepancy management
4. Blinding
Type of Blinding Definition Patient Doctor Sponsor
Open label trails
In this trial patient and the
doctor knows about the drug
given.
Yes Yes Yes
Single Blinding
In this patient don’t know the
detail about drug given but
doctor knows about it.
No Yes Yes
Double Blinding
In this both patient and
doctor does not know about
the drug given.
No No Yes
5. Important shortcut keys used in OC
Key Use
F1 Help
F3 Copy from previous field
F4 Copy from previous record
F5 Refresh
F6 Insert Record
F7 Enter Query
F8 Execute Query
F9 List out values
F10 Save
F11 Operator comment
F12 Investigator comment
6. Important Definitions
• CDISC : The clinical Data Interchange Standards Consortium
organization develops the standards to streamline for Clinical
research.
• SDTM : The Study Data Tabulation Model defines the standard
structure for human clinical trail data tabulations and for non
clinical study data tabulations
• GLIB : The Global Library is a repository of standard CRF
pages/Edit checks which can be reused at study level.
• PPC: The post production change happens if the protocols are
amended or bugs in database design after the study goes live
7. Important Drug Regulatory Bodies
Country Drug Regulatory Body Short Form
USA Food and Drug Administration FDA
UK Medicines and Health care products
Regulatory agency
MHRA
India Drug Control General of India DCGI
Sweden Medical Products Agency MPA
Ireland Irish Medicines Board IMB
China State Food and Drug Administration SFDA
Japan Pharmaceuticals and Medical Devices
Agency
PMDA
Australia Therapeutic Goods Administration TGA
8. ASP Bridge Advance
1.Works on Quintiles tool.
2.Data extraction and sending
is done by Import/Export team.
3.Instance name for iDMA team
is OCP25 and for GBS team is
OCP32.
1.Works on Novartis OC.
2.Data extraction is done by
Life Science Hub team and
data upload is done by
eLoader team.
3.Instance name is OCPRD1.
9. Important Documents used in
clinical trails
• Study plan specification(SPS) : A spreadsheet document that
contains all the study components specification about the
Project, Program & Study Codes, Branching and Collation List
• Mock CRF: Prototype layout of the CRF created as a
specification for database development
• Data Review Plan(DRP): A document that contains the validation
procedures as per the study protocol
10. Hierarchy for creating a CRF
Create Discrete Value
Groups(DVG)
Create Questions
Create Question Groups
Create Data Collection
Module(DCM)
Create Data Collection
Instrument(DCI)
Create Data Collection
Instrument Book(DCI Book)
11. Defining Data Collection Objects
• DVG: A Discrete Value Group or DVG is a list of the values that constitute
acceptable responses to a Question.
• Question: Questions correspond to the Questions on a CRF for a patient visit. You
must define an Oracle Clinical Question for each Question on a CRF.
• Question Group: Question Groups that contain multiple logically related
Questions to be collected in the same section of a CRF
• DCMs : DCMs (Data Collection Modules) that contain multiple logically related
Question Groups to be collected together.
• DCIs : You can create DCIs (Data Collection Instruments) that contain multiple
logically related DCMs. In general, a DCI corresponds to a CRF.
• DCI Book: You can organize DCIs, which generally correspond to CRFs,
into a casebook and assign them to visits by creating a DCI Book
12. Creation of Data Collection Objects
Data Collection Object Can be created
at GLIB level
Can be created at
Study Level
DVG yes No
Questions Yes No
Question Group Yes No
DCM Yes Yes
DCI Yes Yes
DCI Book no yes
13. DVG
• A Discrete Value Group or DVG is a list of the values that constitute
acceptable responses to a Question. You can assign a DVG or DVG
subset to a Question to limit the allowable responses to the Question.
DVG Subsets : For any DVG, you can create one or more subsets that include only
subset of the values in the base DVG as allowable values. When you assign a DVG to
Question, you must specify the subset number. The system allows only the active
values in that subset as responses to that Question. The base DVG contains the
complete set of responses that could logically be accepted as responses to a type
of Question, and its subsets contain only those responses that are appropriate in
certain circumstances
For example, you could create a DVG called AE_SEV to describe the severity of an
adverse event, with the values Mild, Moderate, Severe, and Life-Threatening. You
could assign this DVG to a Question to be collected during the active part of the
study.
14. Kinds of DVGs
Internal Alpha Thesaurus
This is the most commonly
used type of DVG. You
define the list of values in
Oracle Clinical, in the
Discrete Value Groups
window.
Alpha DVGs are designed
to allow you to capture
information when it is not
possible to collect the
expected type of
response.
They are most commonly
used for two
purposes: entering
information on missing
data and entering
alphabetical characters
for numeric lab test results.
Example: Not Applicable,
Unknown, or Not Done
Thesaurus DVG values are
contained in external
tables that you create
in the Oracle Clinical
database.
15. Fields in the upper portion of the
Maintain Discrete Value Groups window
• Subset Number The system generates the subset number, using 0 for the
base DVG, 1 for the first subset, 2 for the second, and so on.
• DVG Type Select one DVG type from a list of values. These values are
configurable; the Oracle Clinical system administrator can maintain the list in
the installation reference code list DISCRETE VAL GRP TYPE CODE
• Thesaurus or Internal or Alpha Select the kind of DVG from the list of
values
16. Statuses in OC
• There are three possible statuses:
Provisional Active Retired
• Provisional status is the default
status.
• All attributes of
provisional status can be
modified.
• All attributes of
provisional status can be
deleted.
• CRF components
with active status
cannot be modified
• CRF components
with active status
cannot be deleted
• CRF components
with active status
can only be retired
for future use
• Redundant eCRF component
which are no longer used in
the study can be retired
17. Fields in the upper portion of the
Maintain Discrete Value Groups window
• Subsettable? If selected, it will be possible to create subsets of this DVG even after
the DVG is set to Active.
• Expandable If selected, it will be possible to add new values to this DVG even after
the DVG is set to Active.
• Enter by Seq (Enter by Sequence Number?) If selected, if the data entry operator
enters the sequence number then system will populate its respective value.
• Resequence If selected, it will be possible to change the order of the values in this
DVG even after it is set to Active.
• Upper Case If selected, the system forces the value to uppercase, regardless of how it
is entered.
18. Fields in the upper portion of the
Maintain Discrete Value Groups window
• CB Layout (Check Box Layout; available only for DVGs with a display type of CBG.)This setting determines how the boxes are
aligned on the page. You can select either a Vertical or Horizontal grouping, with the labels to either the Left or Right of the boxes
• CB Label Source (Check Box Label Source; available only for DVGs with a display type of CBG.) Choose either the Value or Long
Value of each value as the default prompt, or label, for each box in the group.
• Checked Flag Value This parameter is active only for DVGs with a display type of Flag. The default is deselected. You can specify
the default label value in this field.
19. Fields in the upper portion of the
Maintain Discrete Value Groups window
• Max Value Length The default value is 15 characters. If you need longer
values, you can enter a higher number, up to 80 characters.
• Retirement Reason When you change an Active DVG's status to Retired, the system
prompts you for a reason. Select one from a list that includes: DCI/DCM
Enchantement, DVG Obsolète, Improved Version, Inaccurate Logic, New Questions
Defined, New Standard Established for DCI, New Subset Created, Not Replaced, Poor
Design for DCI, Redundant, Relevant Questions Retired, Replaced, Replaced—
Enhancements, Replaced—Errors.
• Status Comment When you change the status of the DVG, you can enter a free form
text comment.
20. Adding Values to a DVG
• For internal and alpha DVGs, after you save the basic definition you must
enter the values you want to include in the DVG.
• For thesaurus DVGs, do not add values here. Thesaurus DVG values are
contained in an external table.
• For each value, enter the following:
Seq# Value Long
Value
Active? Create Mand
Disc?
22. Creating a New Question
Complete these fields:
• Name: A unique name within the Question's domain, up to 20 characters
• Domain: The Question's domain. Could be standard or study name. In ASP Bridge
Study we give the domain as Standard.
• Status There are three possible statuses:
• Provisional(P)
• Active (A)
• Retired (R)
23. Creating a New Question
Complete these fields:
• Medical Evaluation Type: A classification for how to evaluate medical
responses.
• Intent: A unique description of the meaning or intended use of the Question
• Question Type: From the list of values, select a type
Question Type
• Unit (Data type must be
CHAR.)
• Non-Lab (Data type must be
CHAR or NUMBER.)
• Thesaurus Validation (Data
type must be CHAR.)
• Lab Test (Data type must be
CHAR or NUMBER.)
• Complex. (Data type must
be CHAR, NUMBER, DATE or
TIME.)
• Question Set (Data type
must be CHAR, NUMBER,
DATE or TIME.)
• Date Time (Data type must
be DATE or TIME.)
• Char (Data type must be
CHAR.)
• Extended Text (Data type
must be CHAR.)
24. Creating a New Question
Complete these fields:
• Data Type: Data Type is a database classification: Character, Number, Date, and Time
• Date Time Type: Specifies the expected precision of the response for a Question with
Data Type of DATE or TIME.
• Date Time Fmt (Format): The Date/Time Format must specify at least as much
precision as specified by the Date/Time Type field.
• Len (Length): Set the maximum length for responses to the Question. You can make the
length longer when you add it to a study DCM, but never shorter. Length should include
decimal places also
• Dec Plc (Decimal Places): Specifies the expected maximum number of digits to the
right of the decimal point for a response to a NUMBER Question
25. Creating a New Question
Complete these fields:
• SAS Name: The Question's unique identification, within the Domain, for to access
data through the SAS package; up to 8 characters in length, cannot end with a
number, and can be comprised only of uppercase letters, numbers, underscores, or
hashes.
• SAS Label: SAS variable label for data extraction. The Intent field is the default
value for this field. You can change this field only for provisional and active Questions.
• Extract Macro: Specify the Extract Macro associated with this Question, if any. A
list of values is available.
• Question Set Name: For Questions of type Question Set, enter the name of the
Question Set to which you want to link this parent Question.
26. Creating a New Question
Complete these fields:
• Safety Question?: Check if this is a safety-related Question
• Derived?: Select if this Question serves as the recipient of a response generated by
a Derivation Procedure
27. Creating a New Question
• Defining Extended Attributes Oracle Clinical provides additional attributes that can be associated
with Questions to increase the information available in data extract views. The attributes are columns in
the RESPONSES table.
• By default, the system creates one attribute: VALIDATION_STATUS, which contains information on whether any
discrepancies are logged against the response, and if so, their current status.
• The other attributes available are:
• DVG_LONG_VALUE : Applicable only to Questions associated with a DVG.
• EXCEPTION_VALUE_TEXT: The full value of the response is stored in this column if a discrepancy has been
created of the type that indicates that the value is inconsistent with the database use of the DCM Question. In
particular: Data type discrepancies store the value here with the value text null. Length discrepancies store the
full value here with the value text containing null for numbers and containing the text truncated to the DCM
Question length for characters. All Alpha DVG values are stored in Exception value
• FULL_VALUE_TEXT: It stores the combined value of Value text and Exception Value text
28. Creating and Maintaining Question Groups
• Question Groups organize Questions in a study and collect related data by
grouping related Questions.
• The maximum number of Questions in a Question Group is equal to 255, minus
the number of fields in the key template.
• To create a new Question Group: From the Glib menu, choose Question
Groups, then select Prov Question Group or Question Groups.
• Values to be entered:
• Name • Domain • Status • QG Type
• Expand? • Description • Status
Comment
• Retirement
Reason
Type
29. Types of Question Group
Repeating Question Groups Non-Repeating Question Groups"
• Select Repeating group box if
this Question Group needs to be
collected multiple times in the
DCM; for example, if you are
loading normalized lab data
using this Question Group
• For a repeating Question
Group, you can change
Enforce Max Reps? field only for
Provisional and Active DCMs.
• Unselect Repeating group box
if Question Group needs
collected only one time in the
DCM
• For a non-repeating Question
Group, Enforce Max Reps? field
is display-only, with a value of
unchecked.
30. DCM
• A data collection module (DCM) associates one or more related groups of Questions
to a single clinical study visit. It is the equivalent of the section of a CRF that must be
completed during a single clinical visit.
• Creating a New DCM
• From the Definition menu, select DCMs, and choose DCMs
• Insert a new record and enter a name for the new DCM.
• DCM General Attributes include
• DCM Name: Is the standard name specified from the NOVDD Items ‘PANEL’ section .
• Subset: Subset number is auto populated
• Layout: Layout is provided
31. • Name: DCM name + Subset
• Domain: Study name
• Status : Default status is P(Provisional)
• Short Name: Same as DCM Name
• Type: Provide from the NOVDD panel section
• Description : Provide it from the MOCK CRF description
• Date Order: Default is standard.
32. Configuring Question Groups in a DCM
DCM Question Group attributes include:
• DCM Question Group Name
if it is non repeating question group we can give the name as
question group name + : GLSTNR)
If it is a repeating Question Group we use question group name
+R(Ex: GLSTR)
• Library Question Group Name Same as DCM Question Group
Name
• Question Group Domain Default is standard
• Short Name For ASP bridge studies we use NR for Non repeating
Question Group and use R for Repeating Question Groups
33. • Collect in Subset If this field is checked, responses to the
Questions in the DCM Question Group of this DCM subset are
collected. You can exclude a Question group from this DCM
subset without removing it from its record by deselecting this box.
• Display Seq#:As a data entry operator enters data and tabs, this
number determines the default tab sequence in which the
operator's insertion point moves through the form.
• Repeating Group? Select this box if this Question Group needs to
be collected multiple times in this DCM
• Max Reps Expected it displays the number of repeats for a
repeating question group. If the Enforce Max Reps? Is checked ,
we cannot collect more than the set value specified in the Max
Reps Expected using TAB and this will limit the number of repeats.
34. • Reps to Display: This setting determines the size of the scroll area in terms
of the number of repeats displayed at one time. Its is applicable to
character-based layouts only.
• Enforce Max Reps? (Applies to character-based layouts only.) For a
repeating Question Group, you can change this field only for Provisional
and Active DCMs. For a non-repeating Question Group, this field is
display-only, with a value of unchecked.
• Has Border? Selecting this causes the default layout generator to add a
rectangular border around the Question Group. You can change this
field for both Provisional and Active DCMs.
• Prompt Position This value determines the location of the response field's
label, or prompt text, in the layout.
Select Left for non repeating question groups.
Select above for repeating question groups
• Protect Repeating Defaults? Select this box to protect the displayed
default values serving as prompts from being changed during data
entry
35. • Save Repeat Defaults? Select this box to create a set of rows with
repeating defaults, even if the repeats are blank after data
collection.
• Help Text Add extra information, up to 200 characters, about the
Question Group. The text you enter here is available for display
during data entry. You can change this field only for Provisional
and Active DCMs.
• Normalized? The Normalized? flag is available only for repeating
Question Groups.
36. Configuring DCM Subsets
To create a new subset of a DCM, from the Data Collection Modules window, do the following:
• 1. Put your cursor on an existing DCM that you want to create a subset for. If the DCM has more than one
layout, select the one you want to use for the new subset.
• In the Special menu, choose Create DCM Subset. The system creates a new subset with the subset name
(labeled "Name") given a default value of "Copy original name.“
• Give the new subset a better name and, in the Description field, explain its purpose.
• Click the Question Groups button. If there is a whole Question Group that you do not want to collect in the
subset, deselect its Collect in Subset? box.
• (Optional) In the Questions window you can select a DVG, or list of allowable values, for a Question that is
different in this subset than in other subsets of the same DCM.
• Save your changes.
37. Layouts
Character Layout Graphical Layout
• Used in paper based trails
• We can generate a character-
based layout from a DCM that
becomes the form used for data
entry in Oracle Clinical
• Used in RDC based trails
• We can associate the DCM with a
Data Collection Instrument (DCI)
and generate graphic layout that
becomes the HTML form used for
data entry in RDC Onsite and the
PDF form used to generate the
Patient Data Report
38. Form Layout Templates
• It contain DCI Graphic Layouts and define the areas for DCIs,
headers, and footers
• It is used in Advanced Studies.
• 1.Landscape form layout template : default form layout
template used during DCI layout generation.
• 2. Landscape multiple Mark if not done is checked
• 3. Lab Header: Mark if not done and date of assessment is
checked
• 4. Lab Header Unscheduled: date of assessment is checked.
39. DCI
• A DCI includes one or more DCMs and corresponds to a CRF.
Creating a DCI:
DCI Name: We provide the CRF Title of max. 30 characters with no
special characters
Domain: Study name is automatically populated
Status: Default is provisional(P)
Short Name: This name is displayed in RDC and can be of max. 10
characters
DCI type: Select the type from the list of values(Ex: AE for Adverse
Events)
40. Adding DCMs to DCIs
• In the Maintain Study DCIs window, click the DCI Modules button to
reach the Data Collection Modules window.
• Important attributes in DCI Modules
• Qualifying Value: Unique value
• Show Qual Value: Should always be unchecked
41. DCI Books Enhanced DCI Books
• DCI Book is suitable for paper-
based clinical trials.
• The enhanced DCI Book is
suitable for flexible RDC based
trials
Defining a Study Schedule
Interval: You can organize the expected
progress of patients through a study into Intervals
creating a schedule, or timeline
Phase : The most general Interval is the phase
DCI Books
You can organize DCIs, which generally correspond to CRFs,
into a casebook and assign them to visits (Clinical Planned
Events, or CPEs) by creating a DCI Book.
44. Branching
Conditional
Branching
Interval Rules DCI Rules
It refers to the
activation of a
target question
when we specify
a response to
the value of the
source question
Rules that have
one or more
Intervals as the
target are called
Interval Rules.
Rules that have
one or more
DCIs as the
target are called
DCI Rules.
45. • Conditional Branching:
In conditional branching, you specify a source Question and a response value or
range of values that, when entered as patient data, triggers the activation of a target
Question in the same DCM.
Example: For example, define a Question "Are you pregnant?" with a DVG containing
three values: Yes, No, and I Don't Know.
For the response Yes, define a target of the first Question in a Question Group about
the pregnancy
You can define conditional branching on numeric, time, and date Questions.
graphic layouts character-based layouts
If you are using graphic layouts (for
RDC Onsite), do no define conditional
branching within a repeating
Question Group.
If you are using character-based
layouts (for Oracle Clinical or RDC
Classic data entry) you can define
conditional branching within a
repeating Question Group
46. • Interval Rules
• It is a trigger based on Visit
• Example:
if screen failed don’t enter in other screen visits
DCI rules
• It is a trigger based on DCI
• By defining a DCI rule, you make one or more DCIs conditional, so that
they are expected for a patient only if the patient's own data meets the
criteria specified in the rule trigger. You are not required to define DCI
rules in a flexible study
47. • Defining a DCI Rule Target
• The target portion of a DCI rule specifies the DCI or DCIs enabled
for the patient whose data satisfies the trigger condition. If a
patient's data does not satisfy the trigger condition, the target
DCI(s) are not expected for the patient.
Enable DCI Within CPE
Select this option if you want to
enable the target DCI only
within the same CPE as the
trigger DCI, even if it occurs in
other CPEs.
Enable DCI Across CPEs
Select this option if you want to
enable the target DCI in every
CPE where it is defined
Enable DCI Within CPE Enable DCI Across CPEs
Trigger Visit <Target
Visit
Trigger Visit >Target
Visit