Data Management Considerations For Clinical Trials

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Data Management Considerationsfor Clinical TrialsCLINICAL AND TRANSLATIONAL SCIENCE CENTERBrad Pollock, M.P.H., Ph.D.Department of Public Health Sciences1Clinical and Translational Science CenterThe UC Davis CTSC receives support fromthe NIH National Center for Advancing Translational Sciences (award TR001860).

Topics Data operations Databases Softwareo Spreadsheetso Database management systemso Clinical trials management systems Other considerationsClinical and Translational Science Center2

Common TermsAbbreviationCRFDBQCDMPCSRDCFDefinitionCase Report FormDatabaseQuality ControlData Management PlanClinical Study ReportData Clarification FormClinical and Translational Science Center3

Data Management Overview for Clinical ResearchClinical and Translational Science Center4

Data CollectionForm DesignStatisticalAnalysisData ExportArchiveDataRepositoryBuildSite Data Entry/Export fromEMRCoreDM ProcessDataCleaning/DataQualityMonitoringData ExportDatabaseFreezeInclude policies andprocedures

My Background Biostatistics and epidemiology: oncology,HIV, clinical & translational research Biostatistics core director: Cancer Center BSR CTSA BERD CTSA Informatics Cores National CTSA activities: Chair of the BERD Key Function Committee Co-Chair, Methods & Processes Domain TaskForce PI, intervention trials and etiology studies

Biostatistics Core Functions Clarify hypotheses and objectivesDefine endpointsSelect study/experimental designSample size/power calculationsDevelop analytic plans Monitor studies Efficacy/futility Safety Analyze studies Statistical analysis Writing reports/manuscriptsComputation Design studies

Why Talk About Data Managementin a Biostatistics Seminar Series? You have learned a lot aboutbiostatistics, but for most statisticians,the drudgery and hard work is gettingand preparing study data for statisticalanalysis. 90/10 Rule

Clinical and Translational Research Purpose of clinical and translational researchis to discovery new ways to improve thehealth of individuals and populations We do this by conducting research studies: Hypothesis-generating studies Hypothesis-testing studies**includes clinical trials, intervention trials, etc.

Clinical and Translational Research(continued) Regardless of type of study, the mosteloquently designed study is only asgood as its data. Strength of evidence depends oncomplete and valid data:Data Information Knowledge

Clinical and Translational Research(continued) Data completeness and quality are critical forscientific discovery: Good data with a bad design are worthless Bad data with a good design is even worse Many investigators armed with an electronicspreadsheet think they have they need toconduct reproducible clinical/translationalresearchWrong!

Clinical and Translational Research(continued) What’s sexier? Statistical methods Data management (DM) Data management is easily one of themost overlooked, underappreciatedaspects of clinical and translationalresearchNote: For our discussion, a clinical trial is a specificstudy design within a range of clinical/translationalresearch study types

Statistical Competencies6.Understand the value of data quality and datamanagement.7.Understand the reasons for performing research that isreproducible from data collection through publication ofresults.9.Distinguish between variable types (e.g. continuous, binary,categorical) and understand the implications for selection ofappropriate statistical methods. Extensively covered byrequired coursework.12. Understand issues relating to generalizability of a study,including sampling methods and the amount and type ofmissing data.16. Understand the need to address loss to follow-up.21. Understand the purpose of data and safety monitoringplans.Ender et al, J Clin Trans Res, 2017, 1:146–152

DATA MANAGEMENT

What is Data Management? The development, execution andsupervision of plans, policies, programsand practices that control, protect,deliver, and enhance the value of dataand information assets**Data Management Association, Data Management Body of Knowledge (DAMA-DMBOK), 2008

Who is Involved in Data Management?End-to-End iniciansResearch StaffClinical StaffStatisticiansEpidemiologistsAnalytic StaffResearch ITAnalystsProgrammersDBAsCentral ITCIOCRIO

Data Management within theResearch ProcessProtocolDevelopmentITInvolvementData ManagementProcessFinal StatisticalAnalysis

Data Management Changing Withinthe Research ProcessData managementconsiderations arebeginning to influencethe scienceProtocolDevelopment}Data ManagementProcess{Storage and long-term utilizationaffect the data long after theprotocol’s final analysisFinal StatisticalAnalysis

Data Management Elements Need to maintain functional, flexible, scalable, costefficient set of resources to handle a variety of data: Demographic Clinical/laboratory and -omics EnvironmentalExposome Data quality and compliance with regulatoryrequirements HIPAA, 21 CFR Part 11, FISMA Prospective planning for: Long time horizons Environmental Influences on Child Health Outcomes (ECHO) Interoperability and federation OnCore CTMS Enterprise Research with EPIC and REDCap

Database Management Functions Database design Data elements Relationships (data model) Access control/security/integrity Application development Data captureData curationQueryingReportingAudit Database operations

How Data Are Handled? Paper forms (CRFs) and keypunch Web-front end DBMS Pediatric Oncology Group replaced paperin 1998 Web front-end Oracle back-end Clinical Trials Management System(CTMS)Advancing Technology Client-server DBMS and networkedDBMS

Databases Data elements

Data Elements Common Data Elements (CDE) Try to use standards with ontologies– Common Terminology Criteria for Adverse Events(CTCAE)– Patient-Reported Outcomes Measurement InformationSystem (PROMIS)– International Classification of Diseases for Oncology(ICD-O) Data dictionaries Case Report Forms (eCRFs) Map/link to other information systems(biorepository, EHR) Specialized (study-specific data elements)

Building and Adolescent andYoung Adult OncologyResearch Database Demo

Databases Data elements Database models

Database Model: Data Relationships Three types of relationships:– One to OneOne to ManyDiagnosesResearchSubjectBirthCountryMany to ManyResearchSubjectsProtocolsResearchSubject The relationships of the data reflect the rules of thesystem (your protocol) and not all potential possibilities– NOTE: One of the most expensive things to changeonce underway

Databases Data elements Database models Validation Part of the data plan, multiple methods Curation Goal is to maintain the value of the data over time Organization, annotation, revisions/audit log Reuse, future proofing

Software

“Database Management”SoftwareMicrosoft Excel

Excel Characteristics Advantages Easy to work with Quick start up, low costs Potentially you can force data types Disadvantages Easy to work with No requirement to clearly define needs Will “interpret” data entries for you Will not allow you to automatically override

Examples of Good and BadVariable Names

A spreadsheet withinconsistent date formats

Examples of spreadsheets thatviolate the ’‘no empty cells”recommendation

A tidy version of the above data

BHP1Spreadsheets withnonrectangular layouts

Slide 37BHP1Brad H. Pollock, 3/9/2020

spreadsheet with a rectangularlayout

Dangers of Spreadsheets The dangers are real European Spreadsheet Risks Interest Group keeps a public archive ofspreadsheet “horror stories” (http://www. eusprig.org/horror-stories.htm). Many researchers have examined error rates inspreadsheets– Panko* (2008) reported that in 13 audits of real-worldspreadsheets, an average of 88% contained errors. Popular spreadsheet programs also make certaintypes of errors easy to commit and difficult torectify. Excel converts some gene names to dates and stores dates differentlybetween operating systems, causing problems in downstream analyses(Zeeberg et al. 2004; Woo s/whatknow.htm

Dangers of Spreadsheets (continued) Researchers who use spreadsheets should beaware of these common errors and designspreadsheets that are tidy, consistent, and asresistant to mistakes as possible

“Database Management”Software

REDCap Features Good Points Easy to set up, not resource intensiveRequires a real data dictionaryCentral server engine (security & data integrity)Easy access through web front-end Not so Great Points Display interface not very customizable Layout, limited skip patterns, etc. Each application is a separate instanceAdverse events monitoring difficultNot truly relationalNo data curation, electronic data collection only

REDCap(Research Electronic Data Capture) Online or offline use Regulatory compliance HIPAA, 21 CFR Part 11, FISMA Features: Customizable Automated export procedures, built-in projectcalendar, scheduling module Audit trails Ad hoc reporting tools Branching logic, file uploading, and calculated fields

“Database Management”Software

Clinical Trials Management Systems(CTMS)IMPACT CTMSUses: Planning, preparation, monitoring and reporting ofclinical trials Administrative/financial/portfolio managementcapabilities Electronic case report forms (eCRFs) Interoperate with other systems

Other Considerations forData Operations Standard Operations Procedures (SOPs) Disaster recovery Version control (Surround SCM) Audit Separation of duties DBAs, analysts, statisticians Electronic Sign-offs (Editor Monitor PI) Honest broker role (PHI-related)

How important are researchIT/informatics solutions for novelclinical trial designs?

I-SPY 2 TRIAL(Investigation of Serial Studies to PredictYour Therapeutic Response with ImagingAnd moLecular Analysis 2)I-SPY 2 is a clinical trial for women withnewly diagnosed locally advanced breastcancer (neoadjuvant)

D. Berry, San Antonio, 22 Mar 2013

D. Berry, San Antonio, 22 Mar 2013

I-SPY2 TRIALPopulationof eat surgery

I-SPY2 TRIALPopulationof patientsARDAANPDTOIMVIEZLEYArm 2 graduatesto small focusedPhase 3 trialOutcome:Completeresponseat surgery

I-SPY2 TRIALPopulationof eat surgeryArm 3 dropsfor futility

I-SPY2 TRIALPopulationof patientsARDAANPDTOIMVIEZLEYArm 5 graduatesto small focusedPhase 3 trialOutcome:Completeresponseat surgery

I-SPY2 TRIALPopulationof eat surgeryArm 6 isadded tothe mix

Infrastructure Considerationsfor Biomarker-Based Trials Adaptive randomization is highly dependenton near instantaneous synchronized data Research IT Significant IT infrastructure is required to supportbiology-based risk-stratified or adaptive designs Expensive, but there may be some economies ofscale by establishing a single center to coordinate Repurposing Design facilitates repurposing data and supportingfuture CER

Summary Do not underestimate the 90/10 rule You never want to visit a biostatistician for thefirst time with an already collected set of data Same thing here, plan out your data requirementsand plan BEFORE you start your study Multidisciplinary team: Biostatistician/epidemiologistResearch IT / informaticianData management personnelRegulatory personnel Comprehensive and thoughtful databasedesign is key

Summary Comprehensive and thoughtful databasedesign is key: Database content and documentation Software Hardware Consider capability as well as sustainabilityover the long-haul in how you develop yourdata management plan: Future proof as much as possible Stick to industry standards as much as possible Consider future regulatory issues

Summary (continued) I think that informatics/research IT should becore competencies in clinical andtranslational research. Computational technologies for managingdata are changing faster than technologiesfor analysis. Good data management High quality data High quality data Analytic quality

Clinical and Translational Science Center 1 CLINICAL AND TRANSLATIONAL SCIENCE CENTER Data Management Considerations for Clinical Trials Brad Pollock, M.P.H., Ph.D. Department of Public Health Sciences. The UC Davis CTSC receives support from the NIH National Cent

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