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

CTO - Clinical Trials/Research in the Ontology of Biomedical Investigation. Richard H. Scheuermann U.T. Southwestern Medical Center. Clinical Research IT. Functional Requirements.

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

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  1. CTO - Clinical Trials/Research in the Ontology of Biomedical Investigation Richard H. Scheuermann U.T. Southwestern Medical Center

  2. Clinical Research IT

  3. 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…”

  4. Need for standard representations • Minimum information sets • Standard vocabularies/ontologies • Standard data models

  5. 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)

  6. 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

  7. Ontologies related to research (clinical)

  8. Approach • Transparency and inclusivity (http://www.bioontology.org/wiki/index.php/CTO:Main_Page; Google “CTO wiki”) • Combined top down/bottom up approach • Assembled term lists • Combine terms • Separate homonyms • Combine synonyms • Assigned membership into BFO/OBI branches • Position terms within branches • Define terms

  9. CTO Wiki

  10. Term lists

  11. Study Design • 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 • 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 • Exploratory research • 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 • Randomized clinical trial – controlled comparison of an experimental intervention allowing the assessment of the causes of outcomes • 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

  12. Populations • Recruited population • Randomized population • Enrolled population • Eligible population • Screened population • Completer population • Premature termination population • Excluded population • Excluded post-randomization population • Not-randomized-population • Not-enrolled-population • Not-eligible-population • Analyzed-population • 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

  13. 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.

  14. Assign membership into BFO/OBI branches

  15. Biological marker (CDISC) Study populations (CDISC) Trial coordinator (CDISC) Study variable (CDISC) Drug (RCT) Subject (MUSC)

  16. Study

  17. Case report form (CDISC) Patient file (CDISC) Consent form (CDISC) New drug application (MUSC) Investigational new drug application (MUSC)

  18. 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

  19. Negative findings (MUSC) Positive findings (MUSC) Primary-outcome (RCT) Secondary-outcome (RCT)

  20. 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.

  21. “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

  22. CTO Working Group • 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

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