Presentation on theme: "Data Management seminar 05th October 2011"— Presentation transcript:
1 Data Management seminar 05th October 2011 Gwenlian Stifin & Aude EspinasseSouth East Wales Trials Unit, Cardiff University
2 Data management Overview Aim of the session:General understanding of the principlesunderpinning data management forclinical studies.Overview of the data cycle in a clinicalstudy.Overview of data managementprocedures.
3 BackgroundRegulatory FrameworkGood clinical practice is an international ethical and scientific quality standard for the design, conduct and record of research involving humans.GCP is composed of 13 core principles, of which the following 2 applies specifically to data.
4 GCP – core principles for data BackgroundGCP – core principles for dataThe confidentiality of records that could identifysubjects should be protected, respecting theprivacy and confidentiality rules in accordancewith the applicable regulatory requirement(s).All clinical trial information should be recorded,handled, and stored in a way that allows itsaccurate reporting, interpretation andverification.
6 WHAT IS A CRF?A case report form (CRF) is a printed or electronic form used in a trial to record information about the participant as identified by the study protocol.CRFs allow us to:record data in a manner that is both efficient and accurate.Record data in a manner that is suitable for processing, analysis and reporting.
8 KEY QUESTIONS Designing CRFs, key questions: What data is required to be collected?Only data we specified in the proposal/protocol.Only data required to answer the study question.When will this data be collected?Baseline / follow-up .What Forms will need to be designed.Who is going to collect/complete this form.Are there validated instruments available?How is the data going to be analysed.
10 WHAT IS METADATA? Metadata is structured data to organise and describe the data being collected.It is centralized data management.It is a tool to control and maintain dataentities:Content and variable definitionsValidation rulesMetadata consistently and effectivelydescribes data and reduces the probabilityof the introduction of errors in the dataframework by defining the content andstructure of the target data.
11 Metadata FileName of Trial/Study: PAAD (Probiotics for Antibiotic Associated Diarrhoea) - stage 1Metadata Author: H SNumber of Data Collection Forms for Trial/Study: 10Name of File (Corresponding Data Collection Form): Recruitment CRF 02FormVariableVariable LabelData TypeFormatLengthLinkedSkipValidationTitleNameValueMissingConditionTypeLabelsCodesRecruitment CRF 02dateconsdate of consentdatedd/mm/yyyy10rangewarn if < >sugenderservice user gendercategory1 = Male, 2 = Female1consss1consent for SS10 = no; 1 = yes
12 CRF AND DATABASE DESIGN Study outcomes in protocol define what questions are asked in the CRF.Use of validated scales and questionnaires.User-friendliness and ease of completion important.Database is built to receive data extracted from the CRFs.Database needs to include querying and reporting tools.Data needs to be coded into numbers to facilitate statistical analysis.
13 DATABASE DESIGNDatabase allows for adequate storage of study data and for accurate reporting, interpretation and verification of the data.2 database systems tend to co-exist alongside one another:Study management database: personalinformation, recruitment, data completeness(CRF receipts) follow-up triggers…Clinical database: clinical information (studyoutcomes).
14 DATABASE DESIGN Functionalities to consider in both types of database: Validation rules (Ranges, skips, inconsistencies…).Queries / report.Audit trail.
15 TEST/VALIDATE THE DATABASE CheckRanges, Skips, inconsistencies, missing data i.e. what is on your metadata is exactly what is applied when entering the data on the formCheck output file for data export (for clinical database)Variable names match up/are all thereCoding of categories correctNumbers when alpha requiredWhat is on the form is transferred exactly into CSV / SPSS
17 DATA COLLECTION Validity of data collection must be ensured. Source data is identified and data transcribedcorrectly onto data collection system.Process of data collection/transcription is auditedthroughout the process (monitoring – Source dataverification).Next we will discuss Data collectionIt is very important to ensure the validity of the data collectedYou will need to ensure that source data is identified and data transcribed correctly onto data collection system.It is also very important that the process of data collection/transcription is audited at all timesWe will discuss these points in more detail over the next few slides
18 DATA COLLECTION Before starting data collection Testing SOP and PRA TrainingDuring data collectionAuditTESTINGAfter set-up, test or pilot the system before you use it.Maintain an adequate record of this procedure.Firstly I will discuss what to do before starting data collection, and then what to do during data collectionTestingAfter the data collection system has been set-up and before you start collecting data you must test or pilot the systemYou must also maintain an adequate record of this procedure.
19 DATA COLLECTION SOP and PRA Good idea to write a Standard Operating Procedure or a working practice document detailing how you set up your electronic data capture systems.The appropriate persons need to be trained in these.Need to write a Privacy Risk Assessment, this document includes:Personal data items held in study e.g. name, DOBIndividuals who are granted access to this dataProcedures for colleting, storing, and sharing personal dataHow personal data will be anonomisedIdentifying possible breaches of confidentiality and how these can be reducedIt is a good idea to write a Standard Operating Procedure or a working practice document detailing how you set up your data collection systems.The appropriate persons need to be trained in these.You will need to write a Privacy Risk Assessment, this document includes:Personal data items held in study e.g. name, DOBA list of individuals who are granted access to this dataThe procedures for colleting, storing, and sharing personal dataHow personal data will be anonomisedThe document will also identify possible breaches of confidentiality and how these can be reducedThe PRA needs to be updated on a regular basis, this is normally held in the Trial Master file, although you may have a similar document called a delegate log in the site file
20 DATA COLLECTION TRAINING After piloting, when it is working as it should, next step is to train all users of the systemA record should be kept of the trainingA detailed diagram and description of how data will be collected should be provided at training.After piloting, when the data collection system it is working as it should, the next step is to train all users of the systemA record should be kept of the trainingA detailed diagram and description of how data will be collected should be provided at training.There should be documented training on the importance of security and data protection, we will discuss this more later.
21 Participant flowchart Woman identified and agrees to beapproachedAssessed for eligibility and consentedBaseline data (CAPI)RandomisationInterventionControl34-36 weeks gestation (CATI)Birth (CRF)6 month post partum (CATI)1 year post partum (CATI)2 years post partum (CAPI)BirthRoutineantenatal careFNP visits &usual servicesUsualservices18 month post partum (CATI)FNP visits& routineKeyCAPI: Computer Assisted Personal InterviewCATI: Computer Assisted Telephone InterviewData collectionParticipant flowchartParticipant progressThis is an example of a data collection flowchart used at training
22 DATA COLLECTION AUDITMaintain an audit trail of data changes made in the system.Procedure in place for when a study participant or other operator capturing data, realises that he / she has made a mistake and wants to correct data.Important that original entries are visible or accessible to ensure the changes are traceable.You will need to maintain an audit trail of any changes made in the data collection system.A procedure should be in place to address the situation when a study participant or other operator capturing data, realises that he/she has made a mistake and wants to correct the recorded data.It is important that original entries are visible or accessible, for example in the audit trail, to ensure the changes are traceable.
23 ELECTRONIC DATA COLLECTION WHAT IS THIS? Variety of software and hardware nowbeing used to collect data:PCLaptopsmobile devicesaudiovisualtransmissionweb-based systemsElectronic data collection – what is this?A variety of software and hardware are now being used to collect data, these include different systems such as:PClaptopsmobile devices such as BlackBerrysAudio equipment such as voice recordersVisual equipment such as camcorderstransmissionand web-based systems
24 ELECTRONIC DATA COLLECTION WHAT IS THIS? Some of the fundamental issues we havediscussed are common to all modes ofelectronic data collection as well as datacollection on paper.IMPORTANT: There should be no loss ofquality when an electronic system is inplace of a paper system.The fundamental issues that have been discussed (for example, testing, SOPs, training, and audit) are common to all modes of electronic data collection as well as non-electronic data collection.And importantly, there should be no loss of quality when an electronic system is in place of a paper system.
25 ELECTRONIC DATA COLLECTION SPECIFIC TRAINING ISSUES Training on the importance of security; including the need to protect passwords, as well as enforcement of security systems and processes.System user should confirm that he / she accepts responsibility for data entered using their password.Maintain a list of individuals who are authorised to access data capture system and add to PRA.Ensure that the system can record which user is logged in and when. Timely removal of access no longer required, or no longer permitted.There are however some specific training issues that are relevant to electronic data collection .Training on the importance of security; including the need to protect passwords, as well as enforcement of security systems and processes.The system user should confirm that he/she accepts responsibility for data entered using their password.Ensure that you maintain a list of individuals who are authorised to access your electronic data capture system and list these individuals on your trial’s Privacy Risk Assessment.You should also ensure that the system can record which user is logged in and when. There should be timely removal of access when no longer required, or no longer permitted.
26 DATA ENTRY Different types of data entry exist, (manual /optical mark recognition system,online/offline, etc…).Type of data can also influence the method ofdata entry (numerical, free text, images etc…).It is important to have documented procedures(SOPs) defining who is performing data entry andhow it is performed.Different types of data entry exitsThese include:manualoptical mark recognition system, for example teleformsOnlineOfflineThe type of data collected can also influence the method of data entry.It is important to have documented procedures (SOPs) defining who is performing data entry and how it is performed.
27 DATA ENTRY Data entry procedures should be tested at the earlier design stage, and testing adequatelydocumented before sign-off.Adequate training on these procedures should beprovided.Appropriate quality control procedures have to beset up.So to summarise what we have already discussed:Data entry procedures should be tested at the very start of the design stage, and testing must be adequately documented before sign-off.Adequate training on data collection procedures should be provided.Appropriate quality control procedures also have to be set up.
28 ELECTRONIC DATA ENTRYElectronic entry does not usually have to be a separate ‘data entry phase’, normally entered during collection straight onto an electronic CRF.Data can be entered straight onto a website, or can be entered onto a laptop and uploaded using the internet onto a server.When designing forms to collect data electronically you can include ‘validation rules’. An electronic system can stop the Researcher from proceeding with data collection if they break a validation rule.Electronic data entryElectronic data entry does not usually have to be a separate ‘data entry phase’ as it is normally entered during collection straight onto an electronic CRF.Data can be entered straight onto a website (which is automatically sent to a server), or can be entered only a laptop and uploaded using the internet onto a server.Please also keep in mind that when designing forms to collect data electronically that you can include ‘validation rules’. An electronic system can stop Researcher from proceeding with data collection if the break a validation rule, for example, in a pregnancy study researcher cannot enter a number over 42 when asked for ‘weeks gestation’
29 AFTER DATA COLLECTIONRegular backups should be made of your data, if outsourcing data collection or storage ensure that the company have backup systems in place.After trial has finished using data capture systems, you may need to dispose of these or send them to another company e.g. if they are loaned. Before doing this, you may need to professionally erase the hard drive as it may still contain participant information.May need to archive whatever data you collect, includes both hard copy and electronic data, documents not archived need to be disposed of securely.After data collection, regular backups should be made of your data, if outsourcing any data collection or storage please ensure that the company have backup systems in place.After the trial has finished using the data capture systems, you may need to dispose of these or send them to another company, for example, if they are loaned. Before doing this, you may need to professionally erase the hard drive as it may still contain participant information.You may need to archive whatever data you collect, this includes both hard copy and electronic data, and documents not archived need to be disposed of securely and confidentially.
30 COLLECTING DATA SAFELY The safe collection of data in clinical trials is essential for compliance with Good Clinical Practice (CPMP/ICH/GCP/135/95) and the Data Protection Act 1998.Because of increased use of information technology in the collection of trial data there is a need to have clear guidance on how to safely collect data in this manner.Need to protect your data capture systems from loss or unauthorised access, at the same time ensuring that it is accessible to those who need it.Collecting data safelyThe safe collection of data in clinical trials is essential for compliance with Good Clinical Practice and the Data Protection Act.With increased use of information technology in the collection of trial data there is a need to have clear guidance on how to safely collect data in this manner.You will need to protect your data capture systems from loss or unauthorised access, while at the same time ensuring that it is accessible to those who need it.
31 COLLECTING DATA SAFELY ………CONTINUED Need to protect participants’ identity by using Participant Identifiers (PID). PID’s should be used when communicating with other trial team members.Electronic info particularly vulnerable to security threats:can be physically accessed.could be loss or damage to computer.can be remotely accessed through internet or virus.For each tool that you use to collect data, must ensure that system is password protected and encrypted.You will need to protect participants identity by using Participant Identifiers instead of names, Date of Birth etc. Participant Identifiers should be used when communicating with other trial team members.Electronic information can be particularly vulnerable to security threats, it can be physically accessed, or there could be loss or damage to computer, or can be remotely accessed through internet or virus.For each tool that you use to collect data, for example a PC, you must ensure that the system is password protected, and if possible, encrypted.
33 DATA CLEANING Errors / inconsistencies / missing data spotted at different time points depending on the study andmethods used.Errors should be corrected where possible, but nochanges should be made without properjustification.Appropriate audit trails should be kept to documentchanges in the data (queries form, SPSS syntax…).
34 DATA CLEANING No Yes Data validated Data manager cleans and validates data entered in the databaseProblems found such as missing values or inconsistenciesQueries addressed to sitesSite resolves and sends back the queriesData manager checks queries resolutionCorrections are entered onto the DatabaseNoYesData validated
35 REPORTING DATAThroughout the course of the study it is usually the responsibility of the Data Manager to report on study progress, these kinds of reports include:Recruitment progressFollow-up ratesSAEsData completenessWithdrawalsThroughout the course of the study it is usually the responsibility of the Data Manager to report on study progress, these kinds of reports include:Recruitment progressFollow-up ratesSAEsData completenessWithdrawals