Clinical Data Management

Basics of Clinical Data Management

1. CLINICAL DATA MANAGEMENT D. Divya, M. Pharmacy

2. Introduction Clinical data Management is a process of collecting, entering, validating or cleaning the data obtained in Clinical trial. Pharmaceutical industries relies on the electronically captured data for the evaluation of medicines, there is need to follow good practices in CDM and maintain standards in electronic data capture. These electronic records have to comply with a Code of Federal Regulations, 21 CFR Part 11. Society for Clinical Data Management (SCDM) publishes the Good Clinical Data Management Practices (GCDMP) guidelines, a document providing the standards of good practice within CDM.

3. Introduction Many software tools are available for data management, and these are called Clinical Data Management Systems (CDMS). Commenly used CDM tools are Clintrial Informa Rave Oracle Clinical Openclinica TrialDB PhOSCo eClinical Suite

4. Data Management Plan (DMP/DVP) Set up Phase: CRF design and development Writing edit checks Programming CRF Annotation Data base design User Acceptance Testing (UAT)

5. Data Management Plan (DMP) Conduct Phase : 1) Data Collection and data entry 2) Validation 3) Discrepancy Management 4) Med DRA Coding 5) SAE/Lab data reconsillation Close out phase: Database audit Database freeze/ lock

6. Case Report Form According to ICH-GCP, CRF is a printed or electronic document to record all of the protocol required information to be reported to the sponsor on each trial subject. CRF also known as Data Collection Instrument (DCI).

7. Case Report Form Types of CRF: Standard case report form or general case report forms. Ex : subject enrollment form, eligibility form, subject randomization form, medical history, physical examination, clinical laboratory data, compliance, concomitant medication, adverse events, off study and death.

8. Case Report Form Study specific case report forms Ex : Biomarker forms such as cell differentiation biomarkers, inflammatory cytokines, DNA ploidy analysis, PGE2 levels, proliferation analysis, nuclear morphometry . Pharmacokinetics forms.

9. CRF design and development Header : Protocol ID Site ID Subject ID Patient initials Footer : Investigator signatures Date of signature Version number Page number Main module : Details of the Domain.

10. CRF Guidlines Use of consistent formats, font styles and font sizes. Selection of portrait versus landscape versus combination layouts. Use of clear and concise questions, prompt and instructions. Visual cues should be provided Ex: check boxes, Radio buttons Clear guidance about skip patterns like what to skip and what not to skip should be mentioned at appropriate places. Skips (are instructions provided in the CRF page to maintain the connectivity between pages) should be kept to a minimum by the placement of questions to avoid confusions.

11. CRF Guidlines Separate the columns with thick lines. Provide bold and italiaized instructions. Minimize free text responses . Page numbering if necessary, should be consistent throughout. Avoid using “check all that apply” as it forces assumptions about the clinical data. Specify the unit of measurement . Indicate the number of decimal places to be recorded.

12. CRF Guidlines Use standard date format. Ex : dd /mm/ yy throughout the CRF. Use pre coded answer sets. ex: Yes/No Male/Female Mild/Moderate/Severe Not to split modules/ sections ( a set of one or more related groups of questions that pertain to a single clinical study visit) like Ex : AE section should not be split and laid across pages such that information related to a single AE will have to be collected from different pages.

13. Edit Checks Edit Checks are invaluable tools used to enhance the quality of data or to clean the data. Types of Edit Checks: Univariate edit checks Multivariate edit checks

14. Edit Checks Domain Variable Condition Error msg Resolution/Discrepancy logic Type DM(Demography) Site ID If Site ID is missing Site ID is missing Please enter Site ID Site ID should be provided Univariate CO(Concomitant medication) Therapy provided--Yes Drug name is missing If therapy is provided, please mention the drug name also Drug name should be provided Multivariate

15. Edit Checks Process for defining and implementing edit checks: CDM Prepares draft ECS CDM circulate a draft ECS for review Team meeting to finalize the ECS Team review and approve final ECS CDM issue final ECS

16. CRF Annotation For annotating the case report forms we have to maintain standards. These standards are given by CDISC. CDISC---Clinical Data Interchange Standard Consortium. PRM -----Protocol Representation Model. CDASH---- Clinical Data Acquisition Standard Harmonization. LAB ------Laboratory data model SDTM-----Study Data Tabulation Model ADAM-----Analysis Data Model SEND-----Standards for Exchange of Non Clinical Data. PRM for designing the protocol, CDASH and LAB standards for data collection and exchange, SDTM for tabulating the data, ADAM for analyzing the data.

17. CRF Annotation According to SDTM every variable is defined by 8 capital letters and every domain is defined by 2 capital letters. Annotation Domain: DM= Demography, CO= Concomitant medication etc. Variable: Site ID = SITEID Subject ID = SUBJID Visit type = VISIT Visit date = VISITDTC Date of ICF = RFICDTC etc.

18. DATA BASE DESIGN (DBS/ DDS) Variable name should not exceed 8 capital letters. Special characters other than “ _ “should not be used. Any variable should never start with number. Database designers should understand what data should be collected. What will be the data type, length and response type. What are the key questions to be answered. How will the data be analyzed. What reports will be used.

19. DATA BASE DESIGN (DBS/ DDS) How often a new report be generated. Questioners elements are of 2 types Close end questions Ex: Gender ? Male, female, others. Open end questions Ex: why you are participating in clinical trials ? We can’t anticipate the answers.

20. USER ACCEPTENCY TESTING (UAT) Requirement of entering value checking if the data needed to be entered is possible to be entered in a particular field. Need to check Format of values Range checks Floating decimals. Negative value checks Future date checks

21. USER ACCEPTENCY TESTING (UAT) Confirmation of logic between particular fields. Comparing extracted data to original data. Personal data protection evaluation (PHI- Protected Health Info) Correction of lab values units and ranges

22. Data collection Data collection is done using the CRF that may exist in the form of paper or an electronic version which are translated to the database by means of data entry in house. In the eCRF based CDM, the investigator will be logging into the CDM system and entering the data directly at the site. In eCRF method chances of errors are less and the resolution of discrepanices happens faster.

23. Data Entry CRF tracking: The entries made in the CRF will be monitored by Clinical Research Associate for completeness and filled up CRFs are retrieved and handed over to the CDM team. Data entry: Single Data entry Double Data entry Mostly double data entry is performed where in the data is entered by two operators separately.

24. Validation Data validation is a series of documented tests of the data with the goal of ensuring the quality and integrity of the data. Validation Process is complex and dependant on the data captured, business and regulatory concerns, the data management software used and several other factors.

25. Validation Why does clinical data need Validation ? From a business perspective, the data are how the FDA, other regulators and business partners evaluate the worth of the product. From an ethical perspective clinical data affect treatment decisions, which affect patient health and the patient population.

26. Validation Validation is of 2 types 1)Clinical Data base validation 2)Clinical Data Validation Clinical Data base validation: which means performing the edit checks. Sponsor decides what checks should be used, what code lists are appropriate and what procedures will be used for invalid results.

27. Validation Clinical Data Validation: By Investigator: The investigator should ensure the accuracy, completeness, legibility and timeliness for the data reported to the sponsor in the CRF’s and in all required reports. The investigator should ensure that any data reported on the CRF are consistent with the patient’s medical records and where applicable discrepancies should be explained.

28. Validation By Monitor (CRA): Monitor should check the CRF entries with the source documents and inform the investigator of any errors/omissions and assure that all data are correctly and completely recorded and reported. This is called source data verification (SDV) CRF compared with the original medical records to ensure it is complete and accurate. Through SDV process, the monitor should confirm accurate transcription of data from source files to the CRF.

29. Validation There are 2 methods of SDV. Direct access : the monitor is given direct access to the actual source document and conducts an independent comparison versus the CRF. Indirect access : the monitor is not allowed access either to the actual or to the photocopied source document. Key variables are choosen for which the investigator or member of staff reads the source documents entry while the monitor compares it with the CRF entry.

30. Validation By CDM: CDM data validation activities are an integral part of GCP and fundamental to the delivery of high quality data for statistical analysis and reporting. Attention should be focused on ensuring that the data are a reasonable representation of what actually happened at the investigator site. The aim is to transform data recorded on CRF’s into information that can be used in the final clinical report from which the right conclusions about the new drug can be made. Data clarification queries are is issued to the investigator at various stages in the process, in particular as a result of pre entry review, data entry and running of edit checks.

31. Discrepancy Management This is also called as query resolution. It helps in cleaning the data and gathers enough evidence for the deviations observed in data. Based on the types identified, discrepancies are either flagged to the investigator for clarification or closed in house by Self Evident Corrections (SEC) without sending DCF to the site. Most common SEC’s are obvious spelling errors. For discrepancies that require clarifications from the investigator, DCF will be sent to the site.

32. Disrepancy Management DCF contains the following elements: Site ID: Investigator name: Date: Subject ID: Module name: Reviewer: Comments: Resolution:

33. MedDRA Coding Medical dictionary for regulatory authorities. It is a rich and highly specific standardized medical terminology to facilitate sharing of regulatory information internationally for medical products used by humans. Products covered by the scope of medDra include pharmaceuticals, biological, vaccines and drug device combination products.

34. MedDRA Coding Why we need to code? In order to standardize the data. Easy to exchange from one country to another country, to regulatory agencies and to MNC’s . WHO.DD – Drug dictionary WHO.ART – Adverse Reporting Terminology COSTART – Coding Symbols for Thesaurus of Adverse Reaction Terms.

35. MedDRA Coding Structure: SOC (System Organic Class) HLGT (High Level Group Term) HLT (High Level Term) LLT (Low Level Term) PT (Preferred Term)

36. Lab Data/ SAE Reconcillation Reconcillation is the process for ensuring consistency in clinical trial data between the data collection database and the safety database. Examples of items that have to be reconcilled : AE – Event description Start of event – onset date End of event – stop date Severity – intensity Seriousness criteria – AE Serious Relationship to study drug – investigator casuality .

37. Lab Data/ SAE Reconcillation Concomitant medication Drug name Start date Stop date Dosages Frequency

38. Database Audit Database audits are conducted between soft lock and hard lock (freeze and lock) of the database. Prior to the database audit the auditor should receive the following documents related to the trial and database. Study protocol including amendments. CRF Data management plan Statistical analysis plan Annotated CRF List of coding dictionaries List of the laboratory units List of all electronic and manual plausibility checks SOP’s of all procedures related to data management.

39. Database Audit Auditing Process: Data managers compare data in the database against the CRF and any associated correction forms. Data managers may review listings of text fields. A separate listing review by Clinical Research Associates is often required for study lock

40. Database Audit The most common reconciliation with external systems is for serious adverse events. Data on SAEs are typically stored in both the clinical data management system and also in a separate SAE system. When reconciliation at study close, data management staff look for Cases found in the SAE system but not in the CDM system.

41. Database Audit Events found in the CDM system but not in the SAE system. Deaths reported in one but not the other. Instances where the basic data matched up but where there are differences, such as in onset date.

42. Database Freeze/ Lock Data base locking is usually a two step process. The first step is often referred to as soft lock or database freeze and occurs after all data cleaning, validation and QC activities have been finalized. The second step is called hard lock or data base lock. At this stage the data base is handed over to statistics for data analysis and the data can be un blinded incase of a blinded study.