The document discusses handling third party vendor data in clinical trials. It covers four types of external data including safety laboratory data, PK/PD data, pharmacogenetics data, and device data. Centralized vendors provide standardized testing across sites and electronic transfer of data to minimize errors. Data reconciliation involves generating discrepancy reports using primary keys like sponsor ID, study ID, and subject ID, and secondary keys like date of birth. Queries are raised to sites or vendors to resolve inconsistencies between third party and clinical trial databases.
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.
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.
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 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.
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.
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.
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.
Database Designing in Clinical Data ManagementClinosolIndia
When designing a Clinical Data Management (CDM) database, several key considerations should be taken into account to ensure efficient data capture, storage, and retrieval. Here are some important aspects to consider in CDM database design:
Define Study Requirements:
Understand the specific requirements of the study and the data to be collected. This includes variables, data types, formats, and any specific rules or calculations required for data validation and derivation. Consult with the study team and stakeholders to determine the necessary data elements.
Data Model Design:
Develop a data model that represents the structure and relationships of the data. Use standard data models, such as CDISC (Clinical Data Interchange Standards Consortium) standards, as a foundation. Define entities (e.g., patients, visits, assessments) and attributes (e.g., demographics, lab results) and establish relationships between them.
Data Dictionary:
Create a comprehensive data dictionary that provides a detailed description of each data element, including its name, definition, data type, length, format, allowable values, and any validation or derivation rules. The data dictionary serves as a reference for data entry and validation checks.
Database Schema:
Design the database schema based on the data model and data dictionary. Identify the tables, fields, and relationships needed to store the data. Determine primary and foreign keys to establish relationships between tables. Normalize the schema to reduce redundancy and improve data integrity.
Data Capture Forms:
Design user-friendly data capture forms to facilitate efficient and accurate data entry. Align the form layout with the data model and data dictionary. Include necessary data validation checks and provide clear instructions or prompts for data entry.
Data Validation and Quality Checks:
Incorporate data validation checks to ensure data accuracy and completeness. Implement range checks, format checks, consistency checks, and logic checks to identify and prevent data entry errors. Include data quality control processes to identify and resolve data discrepancies or anomalies.
Security and Access Controls:
Implement appropriate security measures to protect the confidentiality, integrity, and availability of the data. Define user roles and access levels to control data access and modification. Employ encryption, authentication, and audit trails to ensure data security and compliance with regulatory requirements.
Data Extraction and Reporting:
Consider the need for data extraction and reporting capabilities. Design mechanisms to extract data from the database for analysis or reporting purposes. Implement data export functionalities in commonly used formats, such as CSV or Excel, or integrate with reporting tools or systems.
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.
TSDP tells about the essential documents that are required for the #conduct of a clinical trial. For #regulatory medical writing training, contact hello@turacoz.in.
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.
Database Designing in Clinical Data ManagementClinosolIndia
When designing a Clinical Data Management (CDM) database, several key considerations should be taken into account to ensure efficient data capture, storage, and retrieval. Here are some important aspects to consider in CDM database design:
Define Study Requirements:
Understand the specific requirements of the study and the data to be collected. This includes variables, data types, formats, and any specific rules or calculations required for data validation and derivation. Consult with the study team and stakeholders to determine the necessary data elements.
Data Model Design:
Develop a data model that represents the structure and relationships of the data. Use standard data models, such as CDISC (Clinical Data Interchange Standards Consortium) standards, as a foundation. Define entities (e.g., patients, visits, assessments) and attributes (e.g., demographics, lab results) and establish relationships between them.
Data Dictionary:
Create a comprehensive data dictionary that provides a detailed description of each data element, including its name, definition, data type, length, format, allowable values, and any validation or derivation rules. The data dictionary serves as a reference for data entry and validation checks.
Database Schema:
Design the database schema based on the data model and data dictionary. Identify the tables, fields, and relationships needed to store the data. Determine primary and foreign keys to establish relationships between tables. Normalize the schema to reduce redundancy and improve data integrity.
Data Capture Forms:
Design user-friendly data capture forms to facilitate efficient and accurate data entry. Align the form layout with the data model and data dictionary. Include necessary data validation checks and provide clear instructions or prompts for data entry.
Data Validation and Quality Checks:
Incorporate data validation checks to ensure data accuracy and completeness. Implement range checks, format checks, consistency checks, and logic checks to identify and prevent data entry errors. Include data quality control processes to identify and resolve data discrepancies or anomalies.
Security and Access Controls:
Implement appropriate security measures to protect the confidentiality, integrity, and availability of the data. Define user roles and access levels to control data access and modification. Employ encryption, authentication, and audit trails to ensure data security and compliance with regulatory requirements.
Data Extraction and Reporting:
Consider the need for data extraction and reporting capabilities. Design mechanisms to extract data from the database for analysis or reporting purposes. Implement data export functionalities in commonly used formats, such as CSV or Excel, or integrate with reporting tools or systems.
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.
TSDP tells about the essential documents that are required for the #conduct of a clinical trial. For #regulatory medical writing training, contact hello@turacoz.in.
An brief introduction to the clinical data management process is described in this slides. These slides provides you the information regarding the data evaluation in the clinical trials , edit checks and data review finally data locking,then the data is submitted to the concerned regulatory body.
CDM is defined as the process of collection, cleaning, and management of subject data in compliance with regulatory standards.
A database must be accurate, secure, reliable and ready for analysis.
CDMS is the tool for clinical data management.
RESPONSIBILITES OF CDM
STUDY SET UP (15%) - this includes all the activities that are done before at the starting of the study.
STUDY CONDUCT (60%)- this starts once a subject enrollment begins or with first patient‘s first visit.
STUDY CLOSEOUT (25%) -the data is final and ready for statistical analysis.
CASE REPORT FORM
Types of CRF
PAPER CRF
e-CRF
Data entry is a process of entering or transferring data from case report form ( paper or image ) to clinical data management system (electronic storage ).
optical mark reading (OMR)
Data entry may be entirely manual or partly computerized using optical character recognition (OCR).
The three basic types of data entry system:
(a) Local data entry system - data entry is done on site
(b) Central data entry system - data entry is done at data management centre from the received CRFs;
(c) Web based data entry system - data entry is done through web (secure link) using internet connection.
DATA CLEANING
MANAGING ADR DATA
ADR data are collected from clinical trials and marketed products.
All ADR are reported to clinical data management system or safety system.
During clinical trials-ADR information is also received through CRF or EDC.
These information are stored in clinical data management database.
DATABASE LOCK PROCESS
Data manager – data is accurate and complete
Clinical project manager – site activities are complete
Medical monitor - data is medically accurate
Biostatistician - data is ready for evaluation and analysis.
Data manager asks for the database to be locked .
Done through the company’s IT department.
Once locked, no data can be changed.
Signed process for locking the database is placed in Trial Master File (TMF)
Data base closure (database lock): The database closure for the study is done to ensure no manipulation of study data during final analysis.
DATABASE LOCK/FREEZE is a TWO step process:-
The first step is often referred as SOFTLOCK or DATABASE FREEZE- occurs after all data cleaning, validation, and QC activities have been finalized.
The second step is called HARDLOCK or DATABASE LOCK –At this stage the database is handed over to statistics for data analysis.
DATA TRANSFER
Traditional Data Transfer
CRFs developed by sponsor and supplied to the site along with completion/instruction manual .
Use a black or blue ball point pen for permanency – and PRESS HARD.
At the time of a monitoring visit, CRFs are reviewed for adherence to
guidelines and verified against source documents by the Monitor.
During the monitoring visit, site staff make required corrections to CRFs
Verified/corrected CRFs are submitted to the sponsor, leaving a legible
copy of the CRF at the site.
View this recorded webinar to hear an overview of the Guidance Document on Electronic Source Data in Clinical Investigations and its practical implementation.
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.
The 10th Annual Utah Health Services Research Conference: Data Quality in Multi-Site Health Services and Comparative Effectiveness Research: Lessons from PHIS+ By: Ram Gouripeddi
Health Services Research Conference: March 16, 2015
Patient Centered Research Methods Core, University of Utah, CCTS
Revelatory Trends in Clinical Research and Data ManagementSagar Ghotekar
Revelatory Trends in Clinical Research and Data Management
Clinical data management is a heart and important part of a clinical trials, the outcome to generate quality data and accounting of records to protect clinical trial participants data leads to highest quality and integrity of clinical trials.
Data Validation in Clinical Data ManagementClinosolIndia
Data validation is a crucial component of Clinical Data Management (CDM) to ensure the accuracy, completeness, and consistency of clinical trial data. It involves the systematic and rigorous examination of data to identify and address errors, inconsistencies, or discrepancies. Effective data validation enhances the integrity of the clinical trial data and is essential for regulatory compliance and the generation of reliable results. Here are key aspects of data validation in CDM
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
Study start up activities in clinical data managementsoumyapottola
Study start-up (SSU) is so much more than a one-time document management exercise. It’s a global, strategic operation that can get new drugs approved faster – and it’s ripe for innovation – from Site Selection to Site Activation and Site Training.
Many SSU tech solutions deployed by sponsors don’t deliver the results promised because they add burden without benefits to clinical research sites. The result? Site staff simply avoid using them.
When that happens, document exchange and tracking falls back to paper, email and Excel formats – with CRAs holding the processes together. The tools that were supposed to solve a problem become part of the problem – and consume preThe implementation and conduct of a study can be a complex process that involves a
team from various disciplines and multiple steps that are dependent on one another. This
document offers guidance for navigating the study start-up processcious clinical trial budget.
A successful clinical study start-up is a crucial first step and an important factor for the overall success of the trial. For this reason, SCRO has experienced study start-up teams, offering customized services depending on your needs, whether it be fuWhile the definition varies across companies, study startup typically includes the process of identifying and qualifying sites, collecting essential documents at the study and site level, and submitting these documents for ethics approval. Successful study startup requires coordination between sites, sponsors, and contract research organizations (CROs) to achieve critical milestones in a compliant manner.ll-service or single activities.
How to achieve better time management in EDC start up
Clinical data management requires strict time management processes, especially in study start up within an electronic data capture (EDC) system. Three steps that clinical data management teams can take to outline the planning and executing of each task that needs to be considered are as follows:
Make a List: Create a daily or weekly task list and schedule when each task will be completed. This strategy will assist you in maintaining focus and staying organized.
Set realist goals: Be realistic about what you can finish in the amount of time you have. When setting unrealistic goals, failure is almost certain to follow.
Explore time-saving techniques: Examples of techniques that could help save time include grouping similar tasks together or using a timer to stay focused.
To help get started, here is a list of EDC considerations for Study Start-Up deadlines:
Protocol finalization and study enrollment
Split go-live considerations
eCRF Specification meetings (this will ensure proper collaboration and minimize any back-and-forth communication)
EDC add-on modules (which will be required and need validation?)
ePRO/eCOA used with licensed questionnaires.
IRB requirements for add-on modules (eConsent/ePRO)
Role of computer in clinical developmentDivyaShukla61
computers have always played a crucial role in our daily lives, Here i have presented its role in Clinical development.Hope you understand easily from my presentaion.
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
- 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
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Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
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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.
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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
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
3. Often during the conduct of a clinical trial, external data which is not
included in CRFs will be collected. If not included in the primary safety
or efficacy parameters, these data can be used for subject screening,
routine safety and quality-of-life monitoring, or trend analysis.
To speed up the process and minimize the use of different analyzing
methodologies and equipments, it is common for sponsors to refer to
the use of centralized vendors.
The vendors provide electronic transfer of computerized data into the
sponsor’s database, thereby offering quick results, standardized testing,
and reference and calibration values applied to data collected across
study sites with the potential to eliminate transcription errors and key
entry of data.
Overview
3
4. After completing this chapter you will be able to understand:
– The different kinds of Third Party Data in clinical trials
– The need and significance of Laboratory data generated in clinical trial
– The common problems and challenges faced in handling lab data
– PK/Biomarker data
– The reconciliation process of the Third Party Vendor (TPV) data
Objectives
4
5. • Third Party Data also called as non- CRF/eCRF data
• TPV data is transferred from external sources into the sponsor
database via secure server for further processing
• TPV data may or may not be part of your clinical database
• Lab data constitutes as one of the major component of the TPV
data
• Hospitals and physicians office laboratories perform 75% of all
analyses. The remaining 25% are performed by independent
reference laboratories
• The international standard in use today for the accreditation of
medical laboratories is ISO 15189 - Medical laboratories - particular
requirements for quality and competence.
Do You Know
5
6. Types of External Data
6
External data can originate from different sources, but it is a
common practice for a centralized vendor to specialize and
produce one or more major data types. Examples of data types
include:
Safety Laboratory
PK/PD Data (may or may not be provided by the central lab)
PGX (Pharmacogenetics)
Biomarkers
Device Data (ECG, Flowmetry, Vital Signs, Images, and other)
Electronic Patient Diaries
IVRS data (which is majorly used for subject randomization).
7. TPV data reports highlight the inconsistencies between the Third
Party Data (Lab, PK, etc.) and the data captured on the clinical
database. It can be automated where in queries will be generated
within a system.
Using Key Variables, these data are reconciled
Key variables are those data that uniquely describe each sample
record. Key Variables are of 2 types: Primary and Secondary.
Without such variables, it is difficult (if not impossible) to match
patient, sample, and visit with the results records accurately
Completeness in the choice of variables collected and transferred
offers a way to increase the accuracy and overall quality of the
process.
TPV Discrepancy Reports
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8. TPV Discrepancy Reports
8
Primary Key Variables
•Sponsor Name/ID
•Study/Protocol ID
•Site/Investigator ID
•Subject Identifier (Subject
Number, Screening Number or
number assigned by the CRF
used)
•Clinical Event ID (Visit Number)
•Sample ID or Sample number
•Accession number
Secondary Key Variables
•Subject’s Gender
•Subject’s Date of Birth
•Subject’s Initials
•Transmission Data/Time
•Date associated with the Subject
Visit
•Sequence Number (when more
than one observation per record
exists)
TPV data reports have "certain primary and secondary key
variables" which will be considered while reconciling the reports
9. Laboratory data is an integral part of CDM Process as it is used to:
Screen patients to be included or excluded in a trial
Test the efficacy of the investigational drug
Ensure the continued safety of a patient after administration of new treatment
Can be used as an indicator for systemic toxicities
Laboratory data in a clinical trial may be received typically in 2 ways :
• Local Lab data
Sample analyses done in local labs
Data collected in CRF
• Central Lab data
Lab analyses done in a central lab
Data not collected in CRF gets externally loaded into sponsor database
Significance and Types of Laboratory Data
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10. Lab Data Flow for Local Lab
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If data is entered into CRF then
data loading step is not
required.
12. In some studies the laboratory assessments for subjects at each visit
may be performed in a Local laboratory as per study requirements or
as per protocol
Local labs which are general on-site in the hospital, or medical unit
where the patient visit is taking place
The lab samples may be collected at site and sent collectively to the
local lab or the subjects may be directed to a given lab for lab
assessments
The original copy of the lab reports are sent by the local lab to the site
The investigator refers to the lab reports and then enters the results on
the CRF manually
Local Lab Data
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13. In most of the studies, the laboratory assessments for subjects at
each visit may be performed by the contracted Central
laboratory as per the agreement or study requirements.
Examples of Central Lab Vendors are Covance, CCLS, Quintiles
etc.
The lab samples may be collected from site and collectively
batch shipped to the Central lab
TPD Loader will load the data into the Clinical Database for further
analysis
Central Lab Data
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14. Local v/s Central Laboratory
14
Local Laboratory
•Expertise in a Specialized Test
•Faster Analysis
•Quicker Screening of patients
•Tests as per requirement
•Low Cost
•Avoids Transportation Problems
•Examples: Labs at the site -
Hospital
Central Laboratory
•To accelerate Process
•Use of Standard
•Analyzing Methodologies across
sites
•Equipment
•Electronic Transfer of data
•Quick Results
•Standardized Testing
•Standardized Reference &
Calibration Values
•Eliminates Transcription Errors
•Examples: Quintiles, Covance,
Metropolis, etc
15. Before initiating production transfers, test data transfer is requested
from the Third party vendor to validate the execution of the
loading programs.
Upon successful loading of TPD data, Data Manager will start
validation and reconciliation of the data.
Data Managers will raise queries to the site or the central lab
accordingly using the data validation reports or system
generated discrepancies
• Example:
1. Sample collection Date for Visit 3 is 15 Jan 2012 on the CRF,
however, no sample collection date on the lab Report .
Work around possibility - Query site for clarification/ confirmation
on sample
collection date as it is missing in lab data.
Lab Data Reconciliation
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16. • Data Validation Reports:
Reports to populate the discrepancies/inconsistencies between Vendor
and Clinical database e.g. SAS reports, Discrepancy listings etc.
• System Generated Queries:
Programmed checks in the system which fires on the
discrepancies/inconsistencies
between Vendor and Clinical database
• Manual Queries:
Queries raised manually in the system based on the manual review
performed by
the data management , clinical team or the discrepancy populated
from data
validation reports
Lab Data Reconciliation
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18. List of documents that are maintained/required during reconciliation:
Vendor Issue Log
Missing sample tracker
Vendor agreement for data transfer
Edit Check Specification document
Study Protocol
Data Handling Plan
e-CRF completion guidelines
Any other project or study specific documents
Lab Data Reconciliation
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19. Examples (contd.)
• Visit 3 data entered at wrong visit in Vendor database (e.g. Visit 3
date is 3-Dec-2013 in e-CRF, but 3-Dec-2013 visit date is tagged
under Visit 4 at vendor Database)- Query Site for confirmation
• Date of Birth mismatch- Query Site for confirmation
• Sample collection date mismatch- Query Site for confirmation
Lab Data Reconciliation
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20. Bio analytical samples are analyzed to determine
pharmacokinetic (PK) concentration of study molecule in a
biological samples for a particular study.
Bio analytical samples may be analyzed by sponsor in-house labs
or may be analyzed by the Third Party Vendor (TPV).
On receiving PK data, DM team will start reconciliation of the
data using validation reports or system generated queries.
Pharmacokinetic Data
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21. Reconciliation Example:
Sample Taken is ‘Yes’ at Clinical database, Sample Received is ‘No’
at Vendor Database-Query Site for confirmation
Sample Taken is ‘Yes’ at Clinical Database, Sample details missing at
vendor database -Query Site for confirmation
Sample Logged in PK vendor database, but missing in Clinical
Database - Query Site for confirmation
Incomplete Sample Status at PK Bio analytical database- Query
Vendor
PK Reconciliation
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22. Biomarker (is defined as a characteristic that is objectively
measured and evaluated as an indicator of normal biological
processes, pathogenic processes, or pharmacologic responses to
a therapeutic intervention) sample may be analyzed by local lab
or central lab.
If the biomarker data is analyzed by the local lab, then the data
will be entered into the clinical database (collected on CRF) in
most of the cases.
If the biomarker data is analyzed by the central labTPV, TPD
Loader will load the TPV data into the Clinical database.
Reconciliation:
For each study, at the time of each transfer, DM will reconcile
clinical and vendor database using validation reports or system
generated discrepancies
DM will raise queries to the siteTPV accordingly
Biomarker Data and Reconciliation
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23. ECG data will either be analyzed by TPV or local labs
ECG data will either be collected on CRF or handled externally or
loaded into the study database
TPD Loader will load the TPV data into the Clinical database.
Reconciliation:
Reconciliation reports will be generated or automated
discrepancies will fire in the system
DM will raise queries to the site/TPV accordingly
ECG Data
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24. IVRS system provides the real time clinical trials data tracking for
- Patient Randomization
- Drug Dispensing
If the data is analyzed by the TPV, Data Loader will upload this data
in clinical database for further reconciliation
Queries will be raised to site/TPV as per discrepancies fired in
discrepancy reports or automated queries in system
IVRS (Interactive Voice Response System) Reconciliation
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25. Examples
IVRS date mismatch (Vendor and Clinical Database)
Patient status mismatch (Vendor and Clinical Database)
Inconsistency in Actual Medication Pack number and Assigned
Medication Pack Number
IVRS (Interactive Voice Response System) Reconciliation
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26. Common issue with the data reports:
Excel Limitation -Excel 2007 can hold approx. 1050000 records so if
there are more records it will not be captured
Resolution:
Use of SAS programs/Coma Separated Value (CSV) for
reconciliation of large amount of data
Focus should be on quality of data so time required for
reconciliation or processing of data should be agreed upon with
the relevant teams
General Understanding
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27. • Proposing project plan for handling third party deliverables
• Ensures that the deliverables are cascaded, reviewed and
finalized on time by DM Team
• Ensures that phases, system design, development, validation and
Go-Live are accomplished as per agreed project plan
• The test transfers are to be performed & all
discrepancies/inconsistencies should be resolved before receiving
production data transfer from vendor.
• Ensures that live data is acquired from third party vendor in a
timely manner during study conduct and closure phase
Role of Data Manager
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28. Name the 4 types of TPV data (external data)
Name 2 types of Lab and advantages of each
Difference between Local Lab and Central Lab
Name 2 Primary Key Variables and Secondary Key Variables
State True or False -
Bio analytical samples are analyzed to determine the pharmacokinetic
(PK) concentration of study molecule in a biological samples for a
particular study.
Role of Data Loader
Test Your Understanding
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30. In this session we have covered:
Introduction to TPV Data
Different types of Labs used in Clinical Trials
Need and Significance of Lab Data
Difference between Local and Central Lab data flow
Key Variables
Types of TPV Data
How are TPV data reconciled
Role of TPC
Summary
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