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Practical Guide to Clinical Data Management 3rd edition [Hardback]

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(Clinical Development Operations Excellence, California, USA)
  • Formāts: Hardback, 296 pages, height x width: 234x156 mm, weight: 558 g, 3 Tables, black and white; 26 Illustrations, black and white
  • Izdošanas datums: 26-Oct-2011
  • Izdevniecība: CRC Press Inc
  • ISBN-10: 1439848297
  • ISBN-13: 9781439848296
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Practical Guide to Clinical Data Management 3rd editionPalielināt
  • Formāts: Hardback, 296 pages, height x width: 234x156 mm, weight: 558 g, 3 Tables, black and white; 26 Illustrations, black and white
  • Izdošanas datums: 26-Oct-2011
  • Izdevniecība: CRC Press Inc
  • ISBN-10: 1439848297
  • ISBN-13: 9781439848296
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781439848296.html
Keywords:
Fully updated and including new chapters on trial process and current industry and FDA guidelines, the third edition of this comprehensive guide to managing clinical trials provides a practical outline for designing robust systems for organizing and monitoring trial data. The work is divided into five sections covering study start-up, study conduct, closeout, infrastructure concerns, and clinical data management system design. Chapters include illustrations and tables and a series of appendices provide sample forms, standard operating procedure outlines, and HIPPA and CDISC regulation information. Prokscha is a clinical data management specialist with Genentech. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)
Preface xv
Introduction xvii
SECTION I Study Startup
Chapter 1 The Data Management Plan
3(6)
History of Data Management Plans
3(1)
What Goes into a DMP
4(1)
Signing Off on the DMP
5(1)
Revising the DMP
5(1)
DMPs and the Study Files
6(1)
Using DMPs with CROs
6(1)
Quality Assurance and DMPs
7(1)
SOPs for DMPs and Study Files
7(1)
Using Data Management Plans
8(1)
Chapter 2 CRF Design Considerations
9(14)
Primary Goals of CRF Design
9(5)
Collecting Required Data: Visits, Procedures, Fields
10(2)
Protocol Compliance
12(1)
Collecting Analyzable Data
13(1)
Secondary Goal: Reducing Queries
14(2)
Avoiding Duplicate Data
14(1)
Eliminating Missing or Ambiguous Responses
15(1)
CRFs with Data Processing Impact
16(4)
Log Forms
17(1)
Questionnaires
18(1)
Diagrams and Analog Scales
19(1)
Early Termination Visits
20(1)
Revisions to the CRF
20(1)
Quality Assurance for CRFs
21(1)
SOPs on CRF Design
22(1)
Reuse and Refine CRF Modules
22(1)
Chapter 3 Database Design Considerations
23(14)
Making Design Decisions
23(1)
Basic Clinical Database Concepts
24(8)
Field Data Types
24(1)
Numeric Fields
25(1)
Dates
26(2)
Texts
28(1)
Coded Data
28(2)
Identifier Fields
30(1)
Calculated or Derived Values
31(1)
Tall-Skinny versus Short-Fat Tables
32(2)
Using Standards
34(1)
After Deciding on a Design
35(1)
Quality Assurance for Database Design
35(1)
SOPs for Database Design
35(1)
Responsibilities in Database Design
36(1)
Chapter 4 Edit Checks
37(6)
Choosing Edit Checks
37(2)
Missing Values
38(1)
Simple Range Checks
38(1)
Logical Inconsistencies
38(1)
Cross-Form or Cross-Page Checks
39(1)
Protocol Violations
39(1)
Specifying Edit Checks
40(1)
Quality Assurance of Edit Checks
40(1)
SOPs for Edit Checks
40(2)
The Connection to Queries
42(1)
Chapter 5 Preparing to Receive Data
43(12)
Overview of Creating Study Databases
43(1)
Validating Study Databases
44(1)
A Study Validation Plan
45(1)
Database Specifications
45(1)
Paper Studies
45(1)
EDC Studies
46(1)
How Building Impacts Specifications
46(1)
Testing Study Databases
46(2)
Testing Environment
47(1)
Testing Paper Studies
47(1)
Testing EDC Studies
48(1)
Final Steps in Testing
48(1)
Moving to Production
48(1)
Study Database Change Control
49(2)
Quality Assurance for Building Studies
51(1)
SOPs for Preparing for Data
51(1)
Study Creation Is Programming
52(3)
SECTION II Study Conduct
Chapter 6 Receiving Data on Paper
55(10)
Transcribing Data
55(2)
Double Entry
55(1)
OCR Plus Review
56(1)
Single Entry
56(1)
How Close a Match to the CRF?
57(1)
Dealing with Problem Data
58(1)
Illegible Fields
58(1)
Notations in Margins
58(1)
Using Preentry Review
59(1)
Changing Data after Entry
59(1)
Quality Assurance and Quality Control for Entry
60(2)
Audit Plan
60(1)
Audit Process
61(1)
Audit Report
62(1)
SOPs for Data Entry
62(1)
Entry Quality
62(3)
Chapter 7 Overseeing Data Collection
65(8)
Monitoring EDC Data Collection
65(1)
Monitoring Paper Data Collection
65(6)
Paper CRF Workflow
66(1)
Tracking Challenges
67(1)
Repeating Pages
67(1)
Pages with No Data
68(1)
Duplicate Pages
68(1)
Studies without Page Numbers
68(1)
Missing Pages Reports
69(1)
What Pages Do You Expect?
69(1)
CROs and Tracking Pages
70(1)
Principal Investigator Signatures
71(1)
Using Tracking for Quality Assurance and Quality Control
71(1)
SOPs for Overseeing Data Collection
72(1)
Tracking throughout the Process
72(1)
Chapter 8 Cleaning Data
73(14)
Identifying Discrepancies
73(2)
Automatic Checks
74(1)
Manual Queries
74(1)
Clinical and Listing Review
74(1)
Problems during Entry from Paper
75(1)
Discrepancies Identified by External Programs
75(1)
The EDC Query Process
75(2)
Creating Manual Queries
76(1)
Resolving an EDC Query
76(1)
Getting PI Signatures
76(1)
The Paper Query Process
77(5)
Resolving Discrepancies Internally
77(2)
Turning a Discrepancy into a Query
79(1)
Sending Queries to the Sites
80(1)
Resolving Paper Queries
80(1)
Getting PI Signatures
81(1)
Applying the Resolution
81(1)
Tracking Queries
82(1)
Links to Quality Assurance and Quality Control
83(1)
SOPs for Discrepancy Management
84(1)
Using Queries to Improve Efficiency
84(3)
Chapter 9 Managing Lab Data
87(10)
Storing Lab Data
87(4)
Advantages of the Tall-Skinny Format
88(1)
Disadvantages of the Tall-Skinny Format
89(1)
Identifying Lab Tests
90(1)
Storing Units
91(1)
Ranges and Normal Ranges
91(2)
Laboratory IDs
92(1)
Normal Range Storage
92(1)
Using the Normal Ranges
93(1)
Lab Result Trends
93(1)
Using Central Labs
93(1)
Using Specialty Labs
94(1)
Auditing the Lab
94(1)
Monitoring the Data
95(1)
Quality Assurance around Lab Data
95(1)
SOPs for Processing Lab Data
96(1)
Why Lab Data Needs Special Attention
96(1)
Chapter 10 Non-CRF Data
97(6)
Receiving Electronic Files from a Vendor
97(3)
Transferring Files
97(1)
Formatting the Data
98(1)
Loading Data
98(1)
Identifying File Contents
99(1)
Cleaning Non-CRF Data
100(1)
Quality Assurance for External Data
101(1)
SOPs for Non-CRF Data
101(1)
When Non-CRF Data Is outside Data Management
102(1)
Chapter 11 Collecting Adverse Event Data
103(10)
Collecting Adverse Events
103(4)
Adverse Event Forms
104(2)
Special Considerations for Paper AE Forms
106(1)
Storing and Cleaning AE Data
107(1)
Coding Adverse Event Terms
108(1)
Reconciling Serious Adverse Events
109(1)
Methods for Reconciliation
110(1)
Easing the Reconciliation Process
110(1)
Quality Assurance and Quality Control
110(1)
SOPs for AE Data
111(1)
Impact of AEs on Data Management
111(2)
Chapter 12 Creating Reports and Transferring Data
113(10)
Specifying the Contents
113(2)
From Where?
113(1)
Exactly What?
114(1)
When?
114(1)
Standard and Ad Hoc Reports
115(1)
Data Transfers
116(2)
Transfer Checklists
116(1)
Transfer Metrics
117(1)
Quality Control Review of Printed Reports and Presentations
118(1)
SOPs for Reports and Transfers
118(1)
Putting in the Appropriate Effort
118(5)
SECTION III Study Closeout
Chapter 13 Study Database Lock
123(10)
Final Data
123(1)
Final Queries
124(1)
Final Quality Control
124(3)
Database Audits
124(1)
Summary Review
125(1)
Reconciling
126(1)
Final Steps for EDC
127(1)
Using a Checklist to Lock a Study
127(2)
Setting Database Lock
129(1)
Time to Study Database Lock
129(1)
Quality Assurance around Lock
130(1)
SOPs for Study Closeout
130(1)
Reducing Time to Study Lock
131(2)
Chapter 14 After Database Lock
133(6)
Complete Study Files
133(1)
Assess Study Conduct
133(1)
Site eCRF Copies
134(1)
Unlocking
134(2)
Avoiding Unlocks
134(1)
Approval for Unlocking
135(1)
Unlocking for Paper Studies
135(1)
Unlocking for EDC Studies
135(1)
Quality Assurance
136(1)
SOPs for Study Database Unlock
136(1)
Avoid Unlocks
136(3)
SECTION IV Necessary Infrastructure
Chapter 15 Standard Operating Procedures (SOPs)
139(10)
What Is an SOP?
139(1)
SOPs for Data Management
140(1)
Creating Standard Procedures
141(2)
Starting from Scratch
141(2)
Procedures for New CDM Systems
143(1)
Complying with Standard Procedures
143(3)
Training on SOPs
144(1)
Designing for Compliance
144(1)
Proving Compliance
145(1)
How Data Management SOPs Are Different from Clinical SOPs
146(1)
SOPs on SOPs
146(1)
SOP Work Never Ends
147(2)
Chapter 16 Training
149(6)
Who Gets Trained on What?
149(1)
Study-Specific Training
150(2)
How to Train
152(1)
Training Records
153(1)
SOPs on Training
154(1)
Allotting Time for Training
154(1)
Chapter 17 Controlling Access and Security
155(6)
Account Management
155(2)
Usernames
156(1)
Passwords
156(1)
Account Timeouts
157(1)
Access Control
157(2)
How to Grant Access
158(1)
Who Had Access?
158(1)
SOPs and Guidelines for Accounts
159(1)
Taking Security Seriously
159(2)
Chapter 18 Working with CROs
161(10)
The CRO Myth
161(1)
Auditing CROs
162(1)
Defining Responsibilities
163(1)
Oversight and Interaction
163(3)
Study Startup
163(1)
Study Conduct
164(2)
Closing the Study
166(1)
EDC Vendors as CROs
166(1)
CROs as Functional Service Providers
167(1)
SOPs for Working with CROs
167(1)
Benefiting from CROs
167(4)
SECTION V CDM Systems
Chapter 19 Clinical Data Management Systems
171(4)
CDM System Characteristics
171(1)
Where CDM Systems Come From
172(1)
Choosing a CDM System
172(1)
Using CDM Systems Successfully
173(1)
SOPs for CDM Systems
173(1)
CDM Systems Are for More than Data Entry
174(1)
Chapter 20 EDC Systems
175(8)
What Makes EDC Systems Different?
175(3)
Multiple Data Streams
176(1)
Coding
176(1)
Where the Servers Are---Hosting
176(1)
Study Setup
177(1)
The Need for Data Repositories
177(1)
Working with EDC Systems
178(1)
Main Advantages of EDC
179(1)
Some Problems with EDC
179(1)
Will Data Management Groups Disappear?
180(1)
SOPs for EDC
181(1)
Making EDC Successful
181(2)
Chapter 21 Choosing Vendor Products
183(8)
Defining Business Needs
183(1)
Initial Data Gathering
184(1)
Requests for Information
184(1)
Evaluating Responses
185(1)
Extended Demos and Pilots
185(2)
Hands-On Demos
186(1)
Pilots
186(1)
Additional Considerations
187(2)
What Is Missing?
189(1)
Preparing for Implementation
189(2)
Chapter 22 Implementing New Systems
191(8)
Overview and Related Plans
191(1)
Essential Preparation
192(1)
Integration and Extensions
193(1)
Migration of Legacy Data
194(1)
Benefiting from Pilots
194(2)
Validation
196(1)
Preparation for Production
196(1)
Successful Implementation
196(3)
Chapter 23 System Validation
199(8)
What Is Validation?
199(1)
Validation Plans or Protocols
200(4)
Introduction and Scope
200(1)
Assumptions and Risks
201(1)
Business Requirements and Functional Specification
201(1)
Installation
201(1)
Testing Overview
202(1)
Vendor Audit
202(1)
Security Plan
203(1)
SOPs and Guidelines
203(1)
Completion Criteria
203(1)
Maintaining Validation Plans
203(1)
Change Control and Revalidation
204(1)
What Systems to Validate
204(1)
SOPs for Validation
205(1)
Requirements and Benefits
206(1)
Chapter 24 Test Procedures
207(6)
Traceability Matrix
207(1)
Test Script Contents
208(1)
Purchasing Test Scripts
209(1)
Training for Testers
210(1)
Reviewing Results
210(1)
Test Outcome
211(1)
Retaining the Test Materials
212(1)
Chapter 25 Change Control
213(6)
What Changes Should Be Controlled?
213(1)
Changes to Software Systems
213(1)
Changes to Study Databases
214(1)
Documenting the Change
214(3)
Describe or Propose the Change
215(1)
Assess the Impact
215(1)
Plan Testing
216(1)
Document the Outcome
216(1)
Releasing Changes
217(1)
Problem Logs
217(1)
Considering Version Control
218(1)
The Value of Change Control
218(1)
Chapter 26 Coding Dictionaries and Systems
219(10)
Common Coding Dictionaries
219(1)
MedDRA
220(1)
WHO Drug
220(1)
Using Autocoders
220(4)
Collecting the Term
221(1)
Storing the Results
222(1)
Failure to Code
222(2)
Special Considerations for AE Terms
224(1)
Dictionary Maintenance
225(1)
Quality Assurance and Quality Control for Coding
226(1)
SOPs for Coding and Dictionaries
226(1)
Effective Coding
227(2)
Chapter 27 Migrating and Archiving Data
229(6)
Simple Migrations within Systems
229(1)
Why Migrate between Systems?
230(1)
Complex Migrations
231(1)
Migration by Hand
232(1)
Migrating Audit Trails
232(1)
Archiving Data
232(2)
Level of Archive Access
233(1)
What to Archive
233(1)
Migration and Archive Plans
234(1)
Future Directions
234(1)
Appendix A Data Management Plan Outline 235(4)
Appendix B Clinical Data Management SOPs 239(4)
Appendix C CRO-Sponsor Responsibility Matrix 243(4)
Appendix D Implementation Plan Outline 247(2)
Appendix E Validation Plan Outline 249(2)
Appendix F CDISC and HIPAA 251(2)
Bibliography 253(2)
Index 255
Susanne Prokscha is an Independent Consultant working with Genentech in South San Francisco, California.