Clinical Research Computing

A Practitioner's Handbook
 
 
Academic Press
  • 1. Auflage
  • |
  • erschienen am 29. April 2016
  • |
  • 240 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-12-803145-2 (ISBN)
 

Clinical Research Computing: A Practitioner's Handbook deals with the nuts-and-bolts of providing informatics and computing support for clinical research. The subjects that the practitioner must be aware of are not only technological and scientific, but also organizational and managerial. Therefore, the author offers case studies based on real life experiences in order to prepare the readers for the challenges they may face during their experiences either supporting clinical research or supporting electronic record systems. Clinical research computing is the application of computational methods to the broad field of clinical research. With the advent of modern digital computing, and the powerful data collection, storage, and analysis that is possible with it, it becomes more relevant to understand the technical details in order to fully seize its opportunities.


  • Offers case studies, based on real-life examples where possible, to engage the readers with more complex examples
  • Provides studies backed by technical details, e.g., schema diagrams, code snippets or algorithms illustrating particular techniques, to give the readers confidence to employ the techniques described in their own settings
  • Offers didactic content organization and an increasing complexity through the chapters


Dr. Nadkarni has been working in the field of biomedical informatics since 1989, with over 100 peer-reviewed publications in the field. He is the lead developer of TrialDB, an open-source clinical study data management system, which is used at multiple locations nationally and internationally. He is an Associate Editor of the Journal of the American Medical Informatics Association (JAMIA) since 2005, and was elected Fellow of the American College of Medical Informatics (ACMI) in 2002.
  • Englisch
  • San Diego
  • |
  • USA
Elsevier Science
  • 7,53 MB
978-0-12-803145-2 (9780128031452)
012803145X (012803145X)
weitere Ausgaben werden ermittelt
  • Cover
  • Title Page
  • Copyright Page
  • Content
  • Foreword
  • Motivation
  • Choice of terms
  • The target audience
  • Scope of the book
  • Acknowledgments
  • Bibliography
  • Chapter 1 - An Introduction to Clinical Research Concepts
  • 1.1 - Introduction
  • 1.2 - The level of evidence hierarchy
  • 1.2.1 - Limitations of the evidence hierarchy
  • 1.3 - A bird's-eye view of statistics in clinical research
  • 1.3.1 - Teaching yourself statistics: why
  • 1.3.2 - Teaching yourself statistics: how
  • 1.3.2.1 - Cost considerations
  • 1.3.3 - Hypothesis testing and statistical inference in clinical research
  • 1.3.4 - Type I errors: the choice of the p value
  • 1.3.4.1 - Multiple hypothesis correction
  • 1.3.4.2 - Data dredging
  • 1.3.5 - Type II errors: power analysis and sample size
  • 1.3.6 - Interpreting negative results studies with adequate power and sample size
  • 1.4 - Clinical studies of investigational therapies
  • 1.4.1 - Phase I: early safety testing and dose determination
  • 1.4.2 - Phase II: the open therapeutic trial
  • 1.4.3 - Phase III: the comparative/controlled clinical trial
  • 1.4.4 - Phase IV: postmarketing surveillance
  • 1.5 - Clinical studies of established therapies
  • 1.6 - Experimental design of comparative-effectiveness studies
  • 1.6.1 - Factors influencing therapeutic response
  • 1.6.2 - Separate patient groups: stratified randomization
  • 1.6.3 - Chronic conditions: crossover design
  • 1.6.4 - Placebo effects: double-blind designs
  • 1.6.5 - Pragmatic clinical trials
  • 1.7 - Evaluation of medical software
  • 1.8 - Further reading
  • 1.8.1 - Biomedical basics
  • 1.8.2 - Texts on clinical research
  • Bibliography
  • Chapter 2 - Supporting Clinical Research Computing: Technological and Nontechnological Considerations
  • 2.1 - Technological aspects: software development
  • 2.1.1 - Software-construction tasks: the development process
  • 2.1.1.1 - Rapid functional prototyping
  • 2.1.1.2 - Early testing and continuous integration
  • 2.1.1.3 - Evolutionary delivery
  • 2.1.1.4 - "Agile" development
  • 2.1.1.5 - Use the simplest technology that meets requirements
  • 2.2 - Nontechnical factors: overview
  • 2.3 - Attitude: service versus research
  • 2.3.1 - Balancing act
  • 2.4 - Technical skills
  • 2.4.1 - Minimal technical skillset
  • 2.5 - General skills and breadth of knowledge
  • 2.6 - Communication skills
  • 2.7 - Managing people and projects
  • 2.7.1 - Multifunctional teams
  • 2.7.2 - Software project management
  • 2.7.3 - Effective and ineffective teams: interpersonal factors
  • 2.7.4 - Exploitative practices: a selfish reason not to employ them
  • 2.8 - Personality traits
  • 2.8.1 - Personality profiling: a word of warning
  • 2.9 - Negotiation skills
  • 2.10 - Choosing your collaborators
  • 2.10.1 - Supporting junior faculty
  • 2.10.2 - Scoping the project: change control
  • 2.11 - Topics in clinical research support
  • 2.11.1 - Special aspects of supporting investigational drug studies
  • Bibliography
  • Chapter 3 - Core Informatics Technologies: Data Storage
  • 3.1 - Types of data elements: databases 101
  • 3.1.1 - Organization of data: relational databases
  • 3.1.2 - Key columns
  • 3.1.3 - Why employ auto-number columns for keys?
  • 3.1.4 - Database schemas
  • 3.1.4.1 - Benefits of a schema
  • 3.1.4.2 - Drawbacks
  • 3.1.5 - "Schema-less" design approaches
  • 3.1.5.1 - Entity-attribute-value modeling
  • 3.1.5.2 - Schema-less databases
  • 3.2 - Transactional databases versus analytical databases
  • 3.2.1 - Transaction processing: basic concepts
  • 3.2.1.1 - Isolation: easier said than done
  • 3.2.1.2 - Limiting the degree of isolation to reduce wait times
  • 3.2.1.3 - Extending optimistic concurrency to documents
  • 3.2.2 - Transaction processing across distributed hardware
  • 3.2.2.1 - The CAP theorem: trade-offs
  • 3.3 - Database indexes
  • 3.3.1 - Uses of index-based search
  • 3.3.2 - The B-tree family of indexes
  • 3.3.2.1 - Principles of tree-based search
  • 3.3.2.2 - B+ trees
  • 3.3.2.3 - Indexing trade-offs
  • 3.3.2.4 - Uses of B-tree indexes
  • 3.3.2.5 - Multidimensional B-trees for spatial indexes
  • 3.3.2.6 - Index fragmentation and reorganization
  • 3.3.3 - Hash indexes
  • 3.3.4 - Bitmap indexes
  • 3.3.4.1 - Uses of bitmaps
  • 3.3.5 - Alternatives to indexes: physical links
  • 3.3.6 - Indexing of narrative text
  • 3.3.7 - Indexing of semistructured data
  • 3.4 - Managing integrated (structured + unstructured) data
  • 3.4.1 - Choice of toolset for managing mixed data
  • 3.5 - Nonrelational approaches to data management: "NoSQL" systems
  • 3.5.1 - Types of NoSQL systems
  • 3.5.1.1 - Columnar-store implementation in RDBMSs
  • 3.5.2 - Overview of uses for NoSQL databases
  • 3.6 - Final words
  • Bibliography
  • Chapter 4 - Core Technologies: Machine Learning and Natural Language Processing
  • 4.1 - Introduction to machine learninga
  • 4.2 - The bridge between traditional statistics and machine learning
  • 4.2.1 - Polynomial regression: the overfitting problem
  • 4.2.2 - Cross-validation
  • 4.2.3 - Bias
  • 4.3 - A basic glossary of machine learning
  • 4.3.1 - Sensitivity, specificity, and F1
  • 4.3.1.1 - Significance of positive predictive value
  • 4.3.1.2 - Determining the accuracy of a prediction method or test
  • 4.3.2 - Supervised versus unsupervised methods
  • 4.3.2.1 - Semi-supervised methods
  • 4.4 - Regression-based methods
  • 4.4.1 - Using regression methods for categorical input variables
  • 4.5 - Regression-type methods for categorical outcome variables
  • 4.5.1 - Logistic regression
  • 4.5.2 - Support vector machines
  • 4.5.3 - SVMs versus logistic regression
  • 4.6 - Artificial neural networks
  • 4.7 - Bayes' theorem and Naïve Bayes methods
  • 4.7.1 - Conditional independence and Naïve Bayes
  • 4.8 - Methods for sequential data
  • 4.8.1 - Markov chains and N-grams
  • 4.8.2 - Hidden Markov models
  • 4.8.2.1 - Basic assumptions in employing an HMM
  • 4.8.2.2 - Subproblems with a hidden Markov model
  • 4.8.2.3 - HMM issues with multiple features
  • 4.8.3 - Conditional random fields
  • 4.9 - Introduction to natural language processing
  • 4.9.1 - Eclipse of manually created rule-based approaches
  • 4.9.2 - Information retrieval versus NLP
  • 4.10 - Further reading
  • Bibliography
  • Chapter 5 - Software for Patient Care Versus Software for Clinical Research Support: Similarities and Differences
  • 5.1 - Introduction
  • 5.2 - Similarities between EHRs and CSDMSs
  • 5.2.1 - Data model
  • 5.2.2 - Separation of transactional and query/analytic functions
  • 5.3 - EHRs are specialized for clinical care and workup
  • 5.4 - CSDMSs: study participants (subjects) are not necessarily patients
  • 5.5 - Study protocol: overview
  • 5.6 - Configuration information
  • 5.7 - Recruitment and eligibility
  • 5.8 - Study calendar
  • 5.8.1 - Patient calendar
  • 5.8.2 - Differences between EHR and CSDMS data capture
  • 5.8.2.1 - Use of structured data versus narrative prose
  • 5.8.2.2 - Granularity of data capture
  • 5.8.2.3 - Form reuse issues
  • 5.8.2.4 - Subject/patient-entered data
  • 5.8.3 - Quality-control considerations
  • 5.9 - Multiinstitutional or multinational research scenarios
  • 5.9.1 - Site restriction
  • 5.9.2 - Optimizing software design effort
  • 5.9.3 - Cultural/language issues: localization
  • Bibliography
  • Chapter 6 - Clinical Research Information Systems: Using Electronic Health Records for Research
  • 6.1 - Biospecimen management systems
  • 6.1.1 - Functions provided by biospecimen management systems
  • 6.2 - Grants management systems
  • 6.3 - Clinical research workflow support systems
  • 6.3.1 - Service request management systems
  • 6.4 - Clinical study data management systems
  • 6.4.1 - Study setup: the need for maximal structure
  • 6.4.2 - Implementing REDCap
  • 6.5 - Using EHRs for research
  • 6.5.1 - Using EHRs instead of CSDMSs for primary clinical-study data capture
  • 6.5.1.1 - Pragmatic clinical trials
  • 6.5.1.2 - Quality-improvement initiatives
  • 6.5.2 - Utilizing the EHR's relational data store for data extraction: some tips
  • 6.6 - Effective interoperation between a CSDMS and EHR-related software
  • 6.6.1 - Transferring data from CSDMSs to EHRs
  • Bibliography
  • Chapter 7 - Computer Security, Data Protection, and Privacy Issues
  • 7.1 - Security basics
  • 7.1.1 - Message/content integrity
  • 7.1.2 - Encryption methods
  • 7.1.3 - Digital signatures
  • 7.2 - Special concerns related to personal data
  • 7.3 - Protecting data
  • 7.4 - Institutional preparedness
  • 7.5 - HIPAA matters: calibrating the level of privacy to the level of acceptable risk
  • 7.5.1 - Working with narrative text
  • 7.5.2 - Date-shifting: a warning
  • 7.6 - A primer on electronic intrusion
  • 7.6.1 - Social engineering techniques
  • 7.6.2 - Notable electronic exploits
  • 7.6.3 - Scope of the problem
  • 7.7 - State of healthcare systems with respect to intrusion resistance
  • 7.8 - Role of the US Government
  • Bibliography
  • Chapter 8 - Mobile Technologies and Clinical Computing
  • 8.1 - Introduction
  • 8.2 - Uses of mobile devices: historical and recent
  • 8.2.1 - Using the sensors of mobile devices
  • 8.3 - Applications in biomedical research
  • 8.3.1 - Traditional applications
  • 8.3.1.1 - Patient-entered data
  • 8.3.1.2 - Provider-entered data
  • 8.3.2 - Sensor-based applications
  • 8.4 - Limitations of mobile devices
  • 8.4.1 - Display issues
  • 8.4.2 - Keyboard input issues
  • 8.4.3 - Limitations of voice input
  • 8.4.4 - Specific concerns for wearable devices
  • 8.4.5 - Security issues
  • Bibliography
  • Chapter 9 - Clinical Data Repositories: Warehouses, Registries, and the Use of Standards
  • 9.1 - Introduction
  • 9.2 - Operational data store
  • 9.3 - Data warehouses and data marts
  • 9.3.1 - Warehouses and marts designs
  • 9.3.2 - The mechanics of data warehousing and ETL
  • 9.3.2.1 - Incremental extraction versus full extraction
  • 9.3.2.2 - Physical integration versus virtual integration
  • 9.3.2.3 - Warehousing will not make custom reporting and data extraction go away
  • 9.3.2.4 - Chronological and accuracy issues with EHR and hospital administrative data
  • 9.3.2.5 - Redundancy and pseudoredundancy within the EHR
  • 9.4 - Clinical registries
  • 9.4.1 - Archaic data formats
  • 9.5 - Encoding data prior to warehousing: standardization challenges
  • 9.6 - Relationships between healthcare IT and health informatics groups
  • Bibliography
  • Chapter 10 - Core Technologies: Data Mining and "Big Data"
  • 10.1 - Introduction
  • 10.1.1 - The four Vs of big data
  • 10.2 - An overview of data-mining methodology
  • 10.2.1 - Big data and Bayesian statistics
  • 10.2.2 - Data mining problems and techniques: principles
  • 10.2.3 - Selected data-mining problems
  • 10.2.3.1 - Latent semantic indexing
  • 10.2.3.2 - K-Nearest Neighbors
  • 10.3 - Limitations and caveats
  • 10.3.1 - Hypothesis discovery versus hypothesis confirmation
  • 10.3.2 - Data mining and "knowledge discovery"
  • 10.3.2.1 - The needle-in-the-haystack problem
  • 10.3.3 - Aside: data + data-mining technique = publication
  • 10.4 - The human component
  • 10.4.1 - The problem of overt or subconscious bias
  • 10.5 - Conclusions
  • 10.6 - Additional resources for learning
  • 10.6.1 - "Cookbooks"
  • 10.6.2 - Learning with free or commercial software
  • Bibliography
  • Chapter 11 - Conclusions: The Learning Health System of the Future
  • 11.1 - Introduction
  • 11.2 - Origin and inspiration for the LHS proposal
  • 11.2.1 - Knowledge management
  • 11.2.2 - Business process reengineering (BPR)
  • 11.2.3 - Where BPR and KM have succeeded
  • 11.3 - Challenges of KM/BPR for US healthcare
  • 11.3.1 - Trust between patients and healthcare organizations
  • 11.3.2 - Trust between employees and employer
  • 11.3.3 - Risks of purely technocratic approaches
  • 11.3.4 - Human dimension
  • 11.3.4.1 - Creative process
  • 11.3.4.2 - Conclusions
  • Bibliography
  • Subject Index
  • Back cover

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