ida sim, md, phd associate professor of medicine

Ida Sim, MD, PhDAssociate Professor of Medicine

Director, Center for Clinical and Translational InformaticsUniversity of California, San Francisco, CA

Supported by The Trial Bank Project R01-LM-06780

National Center for Biomedical Ontology U54 HG004028-01

RCT SchemaThe Trial Bank Project

Outline

• Background• RCT Schema

» modeling approach» the class structure» evaluation

• Relationship to CTO • Summary

Goals of the CTO

• (1) fully and faithfully capture the types of entities and relationships involved in clinical trials

• (2) comprehend terms like: cohort, randomization, placebo, etc., including ... statistical terms and terms for ... meta-analysis;

• (3) organize these terms in a structured way, providing definitions and logical relations designed to enhance retrieval of, reasoning with, and integration of the data annotated in its terms

• ... • (6) draw on and seek maximal alignment with existing clinical

trial ontologies, including:» RCT Schema ontology used by theTrial Bank Project

Major Axes for Aligning Models• Domain of clinical trials

» design: (non-)randomized, crossover, cluster randomized, factorial...» objective: interventional, diagnostic, preventive...» clinical domain: drugs, procedures, organizational change...

• Task» trial design, execution, reporting, analysis, application» for individual trials, sets of clinically related trials

• Purpose (application vs. domain ontology)» to support accomplishment of domain task(s)» to define shared meaning for “integration of the data annotated in its

terms”

Systematic Review

Decision Models

Guidelines Electronic Patient Record

Trial Execution

Trial Interpretation

Trial Application

Regulatory Reporting Trial Conduct Trial Design

Feedback to Trial Design

Trial Bank

Scientific Reporting Trial Registration

Trial Tasks

Trial Bank Definition

• Computable repository of RCT information sufficiently detailed to support scientific analysis for » designing future clinical trials» evidence-based practice and policy making

• Detailed information on » study design» study execution» summary and individual participant-level results

• Trial Bank is NOT for running a trial

Systematic Review

Decision Models

Trial Execution

Trial Application

Trial Bank

Trial Bank Target Uses

Trial Bank Software• RCT Bank built on RCT Schema

» Ocelot frame-based ontology

• Bank-a-Trial» web-based program for trialists to enter trial instances into RCT Bank» clinical descriptions of trial features (slot values) are in UMLS

• RCT Presenter» web-based browser of individual trials

Bank-a-Trial RCT Presenter

RCT Bank

RCT Schema

Major Axes for RCT Schema• Domain of clinical trials

» design: (non-)randomized, crossover, cluster randomized, factorial...» objective: interventional, diagnostic, preventive...» clinical domain: drugs, procedures, organizational change...

• Task» trial design, execution, reporting, analysis, application» for individual trials, sets of clinically related trials

• Purpose» to support accomplishment of domain task(s) [application ontology]

» to define shared meaning for “integration of the data annotated in its terms”

Outline

Entity Specification Problem

• What RCT aspects to model in RCT Schema? What not to model?» multiple users (e.g., trialists, systematic reviewers)» multiple tasks (e.g., analysis, interpretation)

– multiple methods» no one correct RCT ontology

• Need principled, systematic approach» to specifying, documenting, and evaluating

Competency Decomposition Method• To define the entities that must be in a conceptual

model• General approach

» specify a task hierarchy of target tasks and subtasks» specify methods for each task» specify entities required for completing each task

using each method• Generates a specification of required entities

» the information requirements for the competencies (tasks and subtasks) that the model/knowledge base is to support

(Sim, et al, JBI 2004: 37(2):108-119)

Systematic Review

Decision Models

Trial Execution

Trial Application

RCT Bank

Target Task for RCT Bank• Using RCTs for trial design or clinical application requires synthesizing evidence across all trials on a topic

Systematic Reviewing

• Canonical method for synthesizing evidence across trials

• Major steps are» retrieve related RCTs (e.g., 32 trials of metformin for

diabetes)» analyze how comparable the trials are» statistically combine data if appropriate

– combining smaller trials increases statistical power to detect effects

Target Task = Systematic Review

• RCT Bank entities = sys. review information needs» identified all systematic review tasks

– review of literature and personal experience conducting 3 systematic reviews

» identified methods for completing these tasks» organized tasks and methods into a task hierarchy» derived RCT entities necessary and sufficient for each

(sub)task

Top-Level Sys Review Tasks• Trial retrieval• Trial critiquing

» judging internal validity » judging generalizability

• Meta-analysis of quantitative results» analysis of clinical and statistical heterogeneity

• Contextual interpretation» scientific, socio-economic, and ethical

http://rctbank.ucsf.edu/tasks/tasks.html

Judgment of Generalizability

• Were the people enrolled in the trial representative and unbiased?» were eligible patients randomly selected from the source

population?» were enrolled subjects a random subset of those

eligible?

• Are the trial subjects similar to mine? • Do I have the tested intervention available here?

Method-(In)dependent Entity Specification• Were the people enrolled in the trial representative

and unbiased?» were eligible patients randomly selected from the

source population?– method: no computable algorithm available

recruitment method» were enrolled patients a random subset of those

eligible?– method: using standard statistics

number and clinical characteristics of enrolled subjects number and clinical characteristics of eligible but non-

enrolled subjects

Results: Entity Requirements

Methods

High-levelTasks

SubTask I

SubTask II

SubTask III

Critiquing

ExternalValidity

InternalValidity

39. . .

QuantitativeSynthesis

Retrieval

ContextualInterpretation

3. . . . .

9. . .

22. . .

112 29 30171 Unique Entities Required

(n=35)

(n=74)

Entity Specification Evaluation

• Evaluated match between » the 171 information items» 388 requirements in 18 published trial-critiquing

instruments

• Results» entity specification is comprehensive» entity specification is reasonable

Sim, et al, KR-MED 2004; JBI 2004: 37(2):108-119

Benefits of Approach• Task hierarchy understandable by domain experts

» identifies which information items are required for which tasks

• Provides an evaluation “yardstick”» if an ontology contains all the information requirements for a

task– then is it “competent” for that task

» can evaluate and compare application ontologies

• Documents an (application/domain) ontology» states which tasks an ontology is competent for, and why» cross-indexes tasks and entities in the ontology

Outline

Implementation of 171 Items• Purpose of RCT Bank KB is to support scientific analysis of

trial evidence» needed a “data-schema” or “instance-style” ontology

• RCT Schema implemented the 171 information items in a frame-based ontology

Bank-a-Trial RCT Presenter

RCT Bank

RCT Schema

RCT Schema Ontology

• Ocelot frame-based model» 7 levels deep

• 192 frames, 607 unique slots» avg. 9.8 slots/frame» 3 frames (1.6%) have multiple parents» 193/607 slots (32%) take other frames as values

• Available at http://rctbank.ucsf.edu/

• RCT Schema displayed in GKB Editor» classes are red boxes» instances are blue boxes

• Class hierarchy organizes entities as » trial concepts» trial descriptions (details)

• Not fully compliant with “Werner’s Rules”

Includes IS-A Hierarchies

Instantiating RCT Schema

• Clinical content described by terms from a clinical vocabulary, e.g., » for a breast cancer trial, instance of BASELINE-

CHARACTERISTIC is described by– term “menopause” from UMLS preferred term– the UMLS CUI

» Trial Bank software supports any vocabulary in UMLS (e.g., SNOMED)

• Each trial is a collection of instances of classes

• 518-TRIAL» 518-

BACKGROUND-DETAILS

» 518-ADMIN-DETAILS

» 518-EXECUTED-PROTOCOL– 518-ALL-SUBJECTS– 518-PRIMARY-

OUTCOME-1– etc.

» 518-ERRATUM» 518-CONCLUSION-

DETAILS

• 518-PRIMARY-OUTCOME-1 (e.g., all-cause mortality)» 518-STAT-ANALYSIS-AND-RESULTS-1 (e.g. t-test)

–518-ALL-COMPARISONS-AT-TIME-X-1 (e.g., at 6 months) 518-SINGLE-TIME-X-COMPARISON-1 (e.g., between

PCI and thrombolysis groups)

• 518-SINGLE-TIME-X-COMPARISON-1 » datapoint for PCI group

– numerator (all-cause deaths at 6 months)– denominator (had all-cause death outcome assessed at 6 months)– 518-STUDY-ARM-POPULATION-1 (the PCI group)

» datapoint for thrombolysis group» summary odds ratio under intention-to-treat analysis» summary odds ratio under efficacy analysis

Outline

• Relationship to CTO• Summary

Characteristic Examples

Clinical domains Cardiology, Radiology, Geriatrics, etc. Intervention Types

Procedures (thrombolysis), Single and Multi-step Drugs (aspirin, warfarin), Counseling, Multiple interventions in one arm

Outcome Types Dichotomous, continuous, univariate, multivariate, survival, regression, scored instruments (e.g., Wechsel Memory Scale)

Result Types Intention-to-treat, efficacy analysis, subgroup analyses

Expressivity Evaluation

• Captured 17 full and 20+ partial trials

Modeling Challenges Met

• Multi-armed, crossover, cluster randomized studies• Many variations of patient drop-out, loss to followup

(e.g., excluded after randomization)• For each outcome, the # of subjects assessed at each

timepoint in each subgroup• Blinding efficacy: did subjects know which arm they

were assigned to?• etc.

Modular, Extensible• Extensions possible with only minimal changes to 192

existing classes

• Extensible to new clinical domains (e.g., genomics) via clinical vocabularies» no clinical terms in RCT Schema

Modeling Extension

New Classes

New Slots

Old Classes Changed

Cluster randomized trials 2 10 3

Complex intervention regimens 4 8 3

Computable eligibilty rules ? ? 3

Limitations of Representation• Modeled, not yet tested

» participant-level data» factorial designs» designs with run-in and washout periods

• In development» computable eligibility rules

• Not yet modeled» genomic data » nested subgroups» secondary studies (e.g,. followup studies)

Trial Bank Publishing

• How to get trials into RCT Bank?• Collaborated with JAMA and Ann Int Med to explore co-

publishing trials as articles and RCT Bank entries» authors submit manuscripts for peer review as usual » trial-bank staff enter accepted trials into trial bank» co-published 14 trials in RCT Presenter

• Evaluation» 83 respondents evaluated a trial using both RCT

Presenter and the Journal Article– mostly trialists and meta-analysts

RCT Presenter Evaluation

85 8165

42 46 42 50

12 1227

3139 50 39

0102030405060708090

ondents

Presenter Better Same as Article

• 70% of respondents rated RCT Presenter as good as or better than the Journal Article for all attributes

Outline

Alignment to CTO

• Have participated in CTO working group from inception (Simona Carini)

• Contributed RCT Schema classes and definitions to list of terms for consideration

• Contributed to draft of high-level concept hierarchy– http://www.bioontology.org/wiki/index.php/High-

level_Concepts_v0.2

• Contributed to creation of the draft CTO presented this morning

How Would Trial Bank Use CTO?• Use CTO as common index into RCT Bank and into

other clinical trial data and information systems • Map RCT Schema class and slot names to terms in

CTO» so that RCT Bank instances can be made available to

machines and humans who wish to – query– reason, or – integrate

» clinical trial information using CTO» e.g., trials co-published with PLoS, etc. into RCT

Example Use (NCBO)

• Map class names from any trial bank to terms in CTO» e.g., in RCT Bank or “European Trial Bank”

– PRIMARY-OUTCOME to CTO term for this– BASELINE-CHARACTERISTIC to CTO term for this

• CTeXplorer an ontology-driven tool for visualizing complex design differences across trials [MA Storey, et al UVic, Canada]

» given variable HgbA1C from any trial bank» would know how to handle and display if annotated as

– PRIMARY-OUTCOME [CTO] or – BASELINE-CHARACTERISTIC [CTO]

CTO for Integration with Trial Bank Collaborators...• National Center for Biomedical Ontology• Electronic Primary Care Research Network

» Primary Care Research Object Model• Global Trial Bank, with AMIA

» trial-bank publishing» “dedicated to assuring the implementation and maintenance

of an open global infrastructure for computable clinical trial results information”

• European Clinical Trial Data Repository» submitted to Framework Programme 7

• with BRIDG, clinical trial management systems, etc?

Summary• Trial banks are computable repositories of trial information

for analysis, interpretation, and application of trial evidence to research and care

• We specified the entities for RCT Bank using competency decomposition method

• RCT Schema implemented entities specification as an “instance-style” frame-based ontology» expressive, extensible, useful for reporting and interpretation

• Mapping of RCT Schema terms to CTO terms makes RCT Bank instances available for CTO-driven integration and reasoning

ida sim, md, phd associate professor of medicine

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