The document discusses the key activities involved in clinical study setup for data management, including designing case report forms (CRFs), developing the study database, programming validation and derivation procedures, and conducting user acceptance testing (UAT). It provides an overview of the study setup process and outlines the objectives, requirements, responsibilities, and deliverables for each setup activity.
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.
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.
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.
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.
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.
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: Best Practices and Key ConsiderationsClinosolIndia
Clinical data management (CDM) is a critical component of clinical research, involving the collection, processing, and analysis of data generated during clinical trials. Implementing best practices and considering key considerations is essential for ensuring data quality, integrity, and regulatory compliance. Here are some important considerations and best practices in clinical data management:
Data Standardization: Standardizing data collection and documentation across study sites is crucial for ensuring consistency and facilitating data analysis. Develop standardized data collection forms, case report forms (CRFs), and electronic data capture (EDC) systems that capture relevant data elements in a consistent manner.
Data Validation and Quality Control: Implement robust data validation procedures to ensure the accuracy and completeness of collected data. Conduct thorough quality control checks, including data validation checks, range checks, and consistency checks, to identify and resolve data discrepancies or errors.
Data Security and Privacy: Ensure data security and protect participant privacy by implementing appropriate measures such as data encryption, secure data transfer protocols, access controls, and adherence to applicable data protection regulations like GDPR or HIPAA.
Data Monitoring and Cleaning: Regularly monitor data collection processes to identify and address data discrepancies, missing data, or outliers. Implement data cleaning procedures to identify and resolve data errors, inconsistencies, and outliers that may impact the integrity and reliability of the study data.
Data Traceability and Audit Trail: Maintain a comprehensive audit trail that captures all changes and activities related to data entry, data modifications, and data review. This ensures data traceability and facilitates data validation and regulatory inspections.
Standard Operating Procedures (SOPs): Develop and adhere to well-defined SOPs for data management activities. SOPs should cover all aspects of data collection, processing, validation, cleaning, and archiving, ensuring consistency and adherence to regulatory requirements.
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.
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.
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
Database design in the context of Clinical Data Management (CDM) is a crucial aspect of organizing and managing clinical trial data effectively and efficiently. A well-designed database ensures that data collected during a clinical trial is accurate, consistent, and accessible, facilitating data analysis, reporting, and regulatory submissions. Clinical Data Management involves various steps, including data collection, validation, cleaning, and reporting
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.
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.
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.
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: Best Practices and Key ConsiderationsClinosolIndia
Clinical data management (CDM) is a critical component of clinical research, involving the collection, processing, and analysis of data generated during clinical trials. Implementing best practices and considering key considerations is essential for ensuring data quality, integrity, and regulatory compliance. Here are some important considerations and best practices in clinical data management:
Data Standardization: Standardizing data collection and documentation across study sites is crucial for ensuring consistency and facilitating data analysis. Develop standardized data collection forms, case report forms (CRFs), and electronic data capture (EDC) systems that capture relevant data elements in a consistent manner.
Data Validation and Quality Control: Implement robust data validation procedures to ensure the accuracy and completeness of collected data. Conduct thorough quality control checks, including data validation checks, range checks, and consistency checks, to identify and resolve data discrepancies or errors.
Data Security and Privacy: Ensure data security and protect participant privacy by implementing appropriate measures such as data encryption, secure data transfer protocols, access controls, and adherence to applicable data protection regulations like GDPR or HIPAA.
Data Monitoring and Cleaning: Regularly monitor data collection processes to identify and address data discrepancies, missing data, or outliers. Implement data cleaning procedures to identify and resolve data errors, inconsistencies, and outliers that may impact the integrity and reliability of the study data.
Data Traceability and Audit Trail: Maintain a comprehensive audit trail that captures all changes and activities related to data entry, data modifications, and data review. This ensures data traceability and facilitates data validation and regulatory inspections.
Standard Operating Procedures (SOPs): Develop and adhere to well-defined SOPs for data management activities. SOPs should cover all aspects of data collection, processing, validation, cleaning, and archiving, ensuring consistency and adherence to regulatory requirements.
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.
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.
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
Database design in the context of Clinical Data Management (CDM) is a crucial aspect of organizing and managing clinical trial data effectively and efficiently. A well-designed database ensures that data collected during a clinical trial is accurate, consistent, and accessible, facilitating data analysis, reporting, and regulatory submissions. Clinical Data Management involves various steps, including data collection, validation, cleaning, and reporting
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.
During the June 2010 quarterly meeting of the Tennessee Board of Regents, board members approved an implementation plan recommended by the National Center for Higher Education Management Systems (NCHEMS) that called for the creation of a data warehouse to be used to enhance decision-making at both the system and campus levels. The strategy now referred to as the “Common Data Repository” (CDR) is to create a single authoritative data warehouse where data from institutions will automatically be fed into the CDR from their Banner administrative systems be they hosted or located at the campus. The presentation provided an overview of the project as to its strategic purpose, how the technology will work, and the role that the functional users will play (including governance).
Excel spreadsheets how to ensure 21 cfr part 11 compliancecomplianceonline123
Learn to create a GxP compliant Excel spreadsheet application. Understand how to validate Excel spreadsheets with minimal documentation. Learn to configure Excel for audit trails, security features, and data entry verification.
Confused by FDA Guidance on Standardized Study Data for Electronic Submissions?Brook White, PMP
In December 2014, FDA released the finalized Guidance for Industry “Providing Regulatory Submissions in Electronic Format—Standardized Study Data.” This presentation reviews key points in the guidance and discusses the implications for Sponsors currently conducting studies as well as those who will be starting new studies soon.
As an expert provider of a wide spectrum of clinical development support services, KCR has developed
a supreme Data Management (DM) solution geared towards full data transparency as well as
delivering the highest level of quality within the defined timelines and in adherence to study budgets,
all the while ensuring the meeting of all Good Clinical Practice (GCP) and ICH requirements. Read our DM brochure and learn more about KCR DM capabilities.
Transform 2014: Best Practices in Integrating Analytics into Your EnvironmentKofax
Getting started with Kofax Analytics for Capture and Kofax Analytics for TotalAgility is the first step to gaining full operational control of your environment. To realize the full potential of these solutions, this presentation will share best practices for effective implementation and preparation for options to extend these solutions to adding Kofax analytics to your capture and BPM implementations. Topics include: planning for the implementation of the base solutions, customization of metrics, dashboards and reports, and the addition of operational data from other systems of record.
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
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
Reconciliation and Literature Review and Signal Detection_Katalyst HLSKatalyst HLS
Introduction Reconciliation and Literature Review and Signal Detection 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
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
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
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
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.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
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.
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
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
- 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
3. Receipt of Good copy of study protocol marks the beginning of study
setup activities for Data Management.
Study set-up activities are critical in a clinical study trial, as it provides
the platform to capture clinical data.
Overview
3
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4. After completing this chapter you will be able to understand:
– DM Activities in Study Set up
– Study Set-up process flow
– CRF designing process flow
– Roles and Responsibilities for CRF designing
– Database designing process flow
– Roles and Responsibilities for DB designing
– Creation of Data Validation Specifications
– Programming of procedures
– Roles and Responsibilities for programming and testing of
procedures
– UAT
Objectives
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5. • It take approximately 16 weeks to complete all the set-up activities
for a Phase 2 paper study without using standard templates.
• The time to design a database is reduced by 40% if standard
templates are available in the library.
Do You Know
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6. Overview of the Study Set-up Process in Clinical Data Management
includes:
– CRF (Case Report Form) Design
– Database design
– Programming Procedures (Validation and Derivation procedures)
– UAT (User Acceptance Testing) / Database testing
DM Activities in Study Set up
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7. STUDY SET-UP OVERVIEW
7
Good Copy of Protocol
Draft CRF specification
Final copy of Protocol
CRF Designing
CRF Finalization and Approval
Metadata and Database Designing
Database UAT
Data Validation Plan
Programming and Edit Checks Testing
Activation of Database Procedure
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8. • A Case Report Form (CRF) is a paper or electronic questionnaire
specifically used in clinical trial research.
• The Case Report Form is the tool used by Clinical Trial Sponsor to
collect data from each participating site.
• All data on each patient participating in a clinical trial are held
and/or documented in the CRF, including adverse events.
• The Clinical Trial Sponsor funds the development of CRF to collect
the specific data they need in order to test their hypotheses or
answer their research questions.
• The size of a CRF can range from a handwritten one-time 'snapshot'
of a patient's physical condition to hundreds of pages of
electronically captured data obtained over a period of weeks or
months.
CRF
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9. Objective of CRF Designing
– Capture all information required by the protocol
– Capture precise, accurate and quality data
– Provide answers to the objectives of the study
– Meet the needs of different clients and end users
– Collect the core data for input into Annual Reports
CRF Designing
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10. The 3 Principle Of Designing CRF (3 C’s): Clear, Concise and Consistent
Elements of the CRF:
• CRF Case Book: Collection of CRFs / DCIs forms a CRF Case Book. CRFs
in the casebook are arranged as per protocol visit schedule (Clinically
Planned Event)
• CRF / DCI: One or more DCM forms a CRF/DCI (Case report form/Data
collection instrument).
• DCM/CRF Section: Data Collection Module is a group of specific
questions collected as per protocol. There are two types of modules i.e.
Safety and Efficacy modules
• Question Groups: A set of related questions grouped together
• Questions: Individual Questions
Principle Of Designing CRF
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12. CRF DEVELOPMENT PROCESS
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Good Copy of Protocol
Mock / Blank CRF
Draft CRF
Draft CRF review by Client
Final Copy of Protocol
Finalize CRF
Annotated CRF
Final CRF Casebook
Client Approval
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13. Requirement
– Approved protocol
– SPS (Study Design specification)
– Mock CRF (Reference to Design a CRF)
Responsibilities:
– Creation of paper Case Report Forms (CRFs)
– Creation of Electronic Case Report Forms (e-CRFs)
– Activation of eCRF/CRF
Pre requisites -CRF Designing
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14. CRF Designing includes:
• Creating DCMs (Data Collection Module) or CRF section as per the
Approved Protocol, SPS & Mock CRF
• DCM consists of Question Groups which in turn contains questions
that collects patient data
• Questions are collected in the DCM based on the requirement of
the SPS and mock CRF
• CRF Pages are arranged as per the sequence defined in the CPEs
(Clinically Planned Event). This sequence is specified in the SPS
• CRFs or DCIs compiled together forms the CRF Casebook or the DCI
Book
CRF Component Designing
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15. Post CRF Designing activities includes:
– The CRF Casebook is sent for the Study Team review and approval
– The review may result in some changes and are taken care by adhering
to the Standards to be followed which are set by the Regulatory
Authorities
– This Casebook is again sent to the Study Team for approval
– Once approved, the CRF is posted into a central repository to be
available for Data Collection. This is specific for Paper CRFs.
– In case of e-CRFs, the questions(fields) are mapped in the database.
This process is not followed for Paper CRFs.
– This marks the closure of the CRF Designing Activity and beginning of
Database Designing activity
CRF Approval
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16. • The annotated CRF is a blank CRF including treatment assignment
forms that maps each blank on the CRF to the corresponding
element in the database.
• The annotated CRF should provide the variable names and coding.
Each page and each blank of the CRF is represented in an
annotated CRF.
• An annotated e-CRF is required when submitting case report
tabulations (CRTs) in an electronic submission for an EDC study.
Annotated CRF
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17. Basic View Of Annotated CRF
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18. Difference between CRF & Annotated
CRF
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19. Deliverables:
– CRF Case Book
– CRF Design QC Checklist (Process specific - May or may not be required
/ present)
CRF Design Deliverables
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20. The CRF designing team usually comprises of the following members.
1. CRF Designer: Member of the DM team. Initiates the draft CRF
preparation.
2. Review Team: Comprises of the LDM, Statistician, DB designer and
sponsor/client. These members will review the draft CRF and
provide inputs or comments if any.
3. Data Manager: Prepares CRF Completion guidelines for the
investigator sites.
Documentation:
Once the CRF has been finalized it is essential to complete the
documentation activities such as filling up the CRF designing checklist, CRF
approval form with appropriate signatures etc. if any.
Roles and Responsibilities
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21. Based on the protocol and CRF a database design document is
prepared also known as the Data Standards Document (DSD) or the
Study Data Specifications (SPS). This may also be referred to as CRF
annotation. The document serves as a guide while designing the data
entry screens.
Pre-requisites for DB designing:
• Data Standards Document
• Active/Final CRF for paper and draft CRF for RDC studies
• Approved protocol
Database Designing
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22. Database Designing Process flow
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Study Data Specification (SPS or
DSD) preparation
Design Study and Import Objects
from Global Library (if applicable)
Prepare e-CRF layouts/data
entry screens as per CRF
QC and testing of Database & UAT
Database Activation
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23. Requirements for RDC Study:
– Final SPS (Study Design Specification)
– Mock CRF
– Approved Protocol
Requirements for non-RDC Study:
– Activated CRF
– Approved Protocol
– Final SPS
Responsibilities :
– Defining Events
– Creation of Data Collection Points and Modules
– Quality check of deliverables
Database Design
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24. Database Designing includes :
• Check that the Study (Protocol Number) and the Intervals are defined
in the Database (e.g. OC – Oracle Clinical)
• Define the CPEs/Visit Matrix and map them to the defined intervals for
the study as per the activated CRF for non-RDC study or as per the SPS
for RDC study
• Defining and Linking Investigators, Sites and Patients to appropriate
centers as per the data provided by the Client
• Select and Create Data Collection Point and DCMs as per the SPS (RDC
Study) or activated CRF (non-RDC Study)
Database Designing
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25. • Compile DCMs to form DCIs (Data Collection Instrument) as per the SPS
(RDC Study) or activated CRF (non-RDC Study)
• Arrange all the DCIs to form a DCI Book as per the SPS (RDC Study) or
activated CRF (non-RDC Study)
• The screenshots are printed for RDC Study and sent to the Study Team
for review
OR
• Once the Database is ready for non-RDC Study it is available to the
Study Team for Review
• The Database is Activated once the approval from the Study Team is
received
Database Designing
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26. Deliverables:
• Database designer QC checklist
• Compiled form of database collection components (Modules)
• Data Entry Guidelines
Database Design Deliverables
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27. DB Designer: Designs the database as per final CRF and SPS/DSD.
Data Manager: Prepares the DSD/SPS and provides it to DB designer.
Tests the database prior to activation and provides findings if any. Is
also responsible for preparing Data Entry guidelines for DE team in case
of paper studies.
Sponsor: Provides approval for DB activation.
Documentation:
On activation of database it is essential to complete all the
documentation such as DB testing findings document, DB testing
checklist, DB QC checklist etc.
Roles and Responsibilities
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28. Objective:
• Automated edit checks are required to flag errors / discrepancies in
the data present on the CRF / database. These checks are referred
to as Validation Procedures or Edit checks.
• Programming these Validation procedures/Edit checks which may
include both Standard and Study specific checks is an important
activity to be completed during Study Set up phase.
Requirements:
• Final DQS/DRP (Data Quality Specification) / DQR (Data Quality
Report)
• DQS / DQR consist of checks that needs to be programmed
Programming Procedures
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29. Responsibilities:
• Programming Validation/Derivation Procedures
• Test Data entry (clean as well as dirty data)
• Retiring and Creation of new version in case of modification
• Activating Validation/Derivation Procedures
Programming Procedures
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30. Procedure writing and Testing Process
flow
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Edit check programmer writes
validation procedures
Data reviewer enters clean and
unclean data in test mode
Validates procedures
Procedures working fine
Procedures are activated
YES
NO Procedures
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31. • Consist of computer checks on data to assure validity & accuracy of
data
• Validate data against predetermined specifications
• Primarily used to check safety and efficacy data unique to current
study
Data Validation Specification (DVS):
A document that describes Data Quality Checks / Derivation for the fields
in the Case Report Form (CRF) to generate electronic errors when
procedures are executed on the data entered in the database.
Procedures:
The computerized program written by Edit Check Programmer (EC
Programmer) for all edits mentioned in the DVP. This program is used to
identify discrepancies in the data.
– Validation procedures are those that will validate the entered data to
generate discrepancies.
– Derivation procedures are those that derive a value from the entered data
Edit checks/Procedures
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33. Programming Procedure includes:
• Programming edit checks for Validation and Derivation Procedures
• Perform test data entry (by entering good as well as dirty data) for the
dummy patients created during database build
• Test the programmed Validation and Derivation Procedures and send it
for approval to the client
• Approved procedures are then activated and is available to be run on
the Production Data
• Activated procedures may require some changes based on the
modification of the requirements or if there is a modification to the
protocol
• In such scenarios retire and create new version of the procedures
• Activate the modified Validation and Derivation procedures once
reviewed and approved
Programming Procedures
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34. Deliverables:
• DQS / DQR containing details of the validation procedures and
derivation procedures
Programming Procedures Deliverables
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35. Requirements:
• Final Data Validation Specifications (DVS)
• DVS consist of checks that needs to be programmed
Responsibilities
• Programmer:
– Programming Validation/Derivation Procedures
– Retiring and Creation of new version in case of modification
– Activating Validation/Derivation Procedures
• Data Manager:
– Test Data entry (clean as well as dirty data)/ i.e. Unit Testing (to
check if implemented programs are working properly. Performed in
Development environment)
Roles and Responsibilities
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36. What is User Acceptance Testing (UAT)
• User Acceptance Testing is often the final step before releasing the
application / database to sites for capturing clinical trial data.
• Usually the end users (Data Manager) who will be using the
applications test the application before ‘accepting’ the
application.
• This type of testing gives the end users the confidence that the
application being delivered to them meets the study and their
requirements.
• This testing is performed by both the technical group designing the
database as well as the Data Managers.
User Acceptance Testing
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37. Clean Data:
Data that is not identified by Validation Procedures as discrepant (i.e.
data that meets the requirement of validation procedures, is without
problems or inconsistencies and therefore should cause no
discrepancies).
Dirty Data:
Data that is identified by validation procedures as discrepant (i.e. Data
which should cause discrepancies).
Definitions
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38. Prerequisites
– Test Scripts / Test cases
– UAT Checklist / DB testing checklist
User Acceptance Testing
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39. What to Test ?
• To ensure an effective User Acceptance Testing Test cases are
created.
• These Test cases can be created using various use cases identified
during the Requirements definition stage.
• The Test cases ensure proper coverage of all the scenarios during
testing.
• During this type of testing the specific focus is the exact real life
usage of the application.
• The Testing is done in an environment that simulates the production
environment.
• The Test cases are written using real life scenarios for the application
User Acceptance Testing
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40. How to Test ?
• The user acceptance testing is usually a black box type of testing. In
other words, the focus is on the functionality and the usability of the
application rather than the technical aspects. It is generally
assumed that the application would have already undergone Unit,
Integration and System Level Testing.
• However, it is useful if the User Acceptance Testing is carried out in
an environment that closely resembles the real life or production
environment.
User Acceptance Testing
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UAT
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41. The steps taken for User Acceptance Testing typically involve one or
more of the following:
– Designing UA Test Cases
– Executing Test Cases
– Documenting the Defects found during UAT
– Resolving the issues/Bug Fixing
– Sign Off
User Acceptance Testing
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43. 1. What are essential elements of CRF designing?
2. What does DSD/SPS stand for?
3. Who is responsible for preparing a SPS/DSD document?
4. What is collection of DCMs known as?
5. Collection of question groups is known as DCI. True or False?
Test Your Understanding
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44. At the end of this session we are now able to understand:
• The processes involved at the study start up.
• CRF designing process.
• Database designing process.
• Procedure writing and testing process.
• Roles and Responsibilities of every individual involved in
the above activities.
Summary
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45. You have successfully completed Study Set-Up
Thank You
&
Questions
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Contact:
Katalyst Healthcare’s & Life Sciences
South Plainfield, NJ, USA 07080.
E-Mail: info@KatalystHLS.com