Transcript CTO - Clinical Trials/Research in the Ontology of Biomedical

CTO - Clinical Trials/Research in the
Ontology of Biomedical Investigation
Richard H. Scheuermann
U.T. Southwestern Medical Center
Clinical Research IT
Functional Requirements
“To take full advantage of the opportunities for translation
created by the molecular biology revolution, standardized
processes to accurately share data within and between
stakeholders institutions is essential. Academic Health
Centers must work together to develop a robust, reliable,
secure, and powerful research IT infrastructure to provide
support for data processing, data communication and
collaborative distributed work environments and the
adoption of standards for sharing data from and about
research projects…”
Need for standard representations
• Minimum information sets
• Standard vocabularies/ontologies
• Standard data models
Clinical Research Data Uses
• Accurate Representation
– therapeutic drug as a design variable vs. medical history
– DNA as a therapeutic agent vs. analysis specimen
• Interoperability
– unambiguous data exchange between research sites
– effective data exchange between software applications
• Customization
– support of study-specific details
• Dynamics
– Role changes throughout and between studies
• Inference
– Semantic queries (e.g. patients with autoimmune disease)
• Meta-analysis
– Studies with common features (e.g. all studies where flu vaccine was
evaluated as a conditional variable)
Constraints
• Essential to build upon and extend, or map to, existing and
emerging data standards (e.g. HL7, CDISC, ICD, UMLS,
Epoch, RCT Schema, NCI Thesaurus, SNOMED-CT, etc.)
• Recognize the difference between Health IT and Research IT
• Support wide variety of different clinical and translational
study types - reduce complexity by modeling commonalities
• Support needs of multiple stakeholders - different uses of
same data
• Standards should be easy to implement and use
• Standards need to be easily and logically extensible
• Support clinical research data use cases
Ontology
Scope
URL
Custodians
Cell Ontology
(CL)
cell types from prokaryotes
to mammals
obo.sourceforge.net/cgibin/detail.cgi?cell
Jonathan Bard, Michael
Ashburner, Oliver Hofman
Chemical Entities of Biological Interest (ChEBI)
molecular entities
ebi.ac.uk/chebi
Paula Dematos,
Rafael Alcantara
Common Anatomy Reference Ontology (CARO)
anatomical structures in
human and model organisms
(under development)
Melissa Haendel, Terry
Hayamizu, Cornelius Rosse,
David Sutherland,
Foundational Model of
Anatomy (FMA)
structure of the human body
fma.biostr.washington.
edu
JLV Mejino Jr.,
Cornelius Rosse
Ontology of Biomedical
Investigation
(OBI)
design, protocol, data
instrumentation, and analysis
fugo.sf.net
OBI Consortium
Gene Ontology
(GO)
cellular components,
molecular functions,
biological processes
www.geneontology.org
Gene Ontology Consortium
Phenotypic Quality
Ontology
(PaTO)
qualities of anatomical
structures
obo.sourceforge.net/cgi
-bin/ detail.cgi?
attribute_and_value
Michael Ashburner, Suzanna
Lewis, Georgios Gkoutos
Protein Ontology
(PrO)
protein types and
modifications
(under development)
Protein Ontology Consortium
Relation Ontology (RO)
relations
obo.sf.net/relationship
Barry Smith, Chris Mungall
RNA Ontology
(RnaO)
three-dimensional RNA
structures
(under development)
RNA Ontology Consortium
Sequence Ontology
(SO)
properties and features of
nucleic sequences
song.sf.net
Karen Eilbeck
Ontologies related to research
(clinical)
Approach
• Transparency and inclusivity
(http://www.bioontology.org/wiki/index.php/CTO:Main_Page;
Google “CTO wiki”)
• Combined top down/bottom up approach
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Assembled term lists
Combine terms
Separate homonyms
Combine synonyms
Assigned membership into BFO/OBI branches
Position terms within branches
Define terms
CTO Wiki
Term lists
Study Design
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Descriptive research – research in which the investigator attempts to describe a group of
individuals based on a set of variable in order to document their characteristics
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Exploratory research
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Case study – description of one or more patients
Developmental research – description of pattern of change over time
Normative research – establishing normal values
Qualitative research – gathering data through interview or observation
Evaluation research – objectively assess a program or policy by describing the needs for the
services or policy, often using surveys or questionnaires
Cohort or case-control studies – establish associations through epidemiological studies
Methodological studies – establish reliability and validity of a new method
Secondary analysis – exploring new relationships in old data
Historical research – reconstructing the past through an assessment of archives or other records
Experimental research
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Randomized clinical trial – controlled comparison of an experimental intervention allowing the
assessment of the causes of outcomes
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Single-subject design
Sequential clinical trial
Evaluation research – assessment of the success of a program or policy
Quasi-experimental research
Meta-analysis – statistically combining findings from several different studies to obtain a
summary analysis
Populations
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Recruited population
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Excluded population
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Randomized population
Enrolled population
Eligible population
Screened population
Completer population
Premature termination population
Excluded post-randomization population
Not-randomized-population
Not-enrolled-population
Not-eligible-population
Analyzed-population
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All subjects
Study arm population
Crossover population
Subgroup population
Intent-to-treat population - based on randomization
per-protocol population - exclude those with serious protocol violations
Homonyms
sample size:
1. A subset of a larger population, selected for investigation to draw
conclusions or make estimates about the larger population.
2. The number of subjects in a clinical trial.
3. Number of subjects required for primary analysis.
Assign membership into BFO/OBI branches
Biological marker (CDISC)
Study populations (CDISC)
Trial coordinator (CDISC)
Study variable (CDISC)
Drug (RCT)
Subject (MUSC)
Study
Case report form (CDISC)
Patient file (CDISC)
Consent form (CDISC)
New drug application (MUSC)
Investigational new drug application (MUSC)
Development plan (CDISC)
Standard operating procedures (CDISC)
Statistical analysis plan (CDISC)
Meta-analysis (CDISC)
Quality assurance (CDISC)
Quality control (CDISC)
Baseline assessment (CDISC)
Validation (CDISC)
Coding (MUSC)
Permuted block randomization (MUSC)
Secondary-study-protocol (RCT)
Intervention-step (RCT)
Blinding-method (RCT)
Study design
Negative findings (MUSC)
Positive findings (MUSC)
Primary-outcome (RCT)
Secondary-outcome (RCT)
Future directions
• Engage more stakeholders
• Continue development
• Evaluation approaches and metrics
– Based on scientific use cases
– Categories of use cases
• Interoperability
– Data exchange
– Accuracy of representation
– Homonyms and context; ontology helps us do
that
• Reasoning and inference
– Test with CTSA IT Project
• Funding - development workshops, etc.
“The opportunities have never been greater to use
modern research advances in genomics and
proteomics and other novel strategies to bring new
insights into the study of disease and human
populations. We need to take advantage of these
opportunities and transform how we practice
medicine.”
EA Zerhouni (2007) Nature 81:126
CTO Working Group
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Jennifer Fostel
Richard Scheuermann
Cristian Cocos
W. Jim Zheng
Wenle Zhao
Jamie Lee
Matthias Brochhausen
Simona Carini
Amar K. Das
Dave Parrish
Ida Sim
Barry Smith
Trish Whetzel