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See all Premium member Presentation Transcript ByDr Amit Bhatt : INTRODUCTION TO CLINICAL DATA MANAGEMENT ByDr Amit Bhatt WHAT IS CLINICAL DATA MANAGEMENT : WHAT IS CLINICAL DATA MANAGEMENT Data management is involved in all aspects of processing the clinical data, working with a range of computer applications, database systems to support collection, cleaning and management of subject or patient data. Types of Studies : Types of Studies Clinical Data Management Paper Based Studies EDC Studies Responsibilities of CDM : Responsibilities of CDM Responsibilities CRF Design / Review Clinical DB Application Development CRF tracking Data Entry Data cleaning Transformation to SAS data Analysis using SAS as per client’s requirement Data submission as per CDISC Standards Security/Confidentiality Data Archiving TYPES OF CRF’s : TYPES OF CRF’s TRADITIONAL PAPER BASED CASE REPORT FORMS (CRF’s) e-CRF (ELECTONIC CASE REPORT FORM) – Study information entered directly into a computer; not paper trail e-CRF is prepared by using: ORACLE CLINICAL OPEN CLINICA CLINTRIAL Slide 6: How many CRFs do you need? – Eligibility or screening form – Physical exam form – Enrollment form – Medical history form – AE form/SAE form – Concomitant therapy form – Blood test form – Laboratory test form – Follow-up visit form Data Base Design : Data Base Design Data from a clinical trial will be collected and stored in some kind of computer system. A data base is simply a structured set of data. A collection of rows and columns . ---- Excel spreadsheet ---- Microsoft Access application ---- Oracle application ---- SAS tables Slide 8: DBMS : MS Access XP, MS Excel XP ORACLE CLINICAL OPEN CLINICA CLINTRIAL CRF Annotation : CRF Annotation CRF Annotation CRF annotation is the first step in translating the CRFs into a database application CDM annotates the CRFs by establishing variable names for each item to be entered. Annotations may include: Field names & attributes (length, data type, dictionary, etc.) Reviewed by CDM and Statistician Data DictionaryDescribe more information about var (name, property, length, etc) : Data DictionaryDescribe more information about var (name, property, length, etc) Data Collection : Data Collection Import baseline data from existing systems Import lab results, scan results and other digital data. CRF Tracking logistic way if it is paper based CRF EDC- electronic data capture if it is e-CRF Data Entry : Data Entry Entry the data with out outliers and missing values. Enter directly using screen forms or Scannable forms. Single data entry Double data entry Data Cleaning : Data Cleaning Data cleaning has to ensure that data submitted from a clinic or laboratory are complete and correct. Error Checking and Validation Database automatically checks data against the range of allowed values or not. Outlier detection. Missing values detection Null values verification All CRFs are reviewed manually Errors found -> DCF Data Validation : Data Validation Consist of computer checks on the data to assure the validity and accuracy of the data Validate data against predetermined specifications Data clarification Form : Data clarification Form Is there any missing values found in CRF prepare DCF Any discrepancies occurred wile entry or checking DCF Discrepancies can be resolved internally by checking source documents or by CRA’s Data clarification Form : Data clarification Form Discrepancies can also be resolved externally by sending DCF to Investigator. Collect the DCF from Investigator. The discrepancy resolutions are then applied to the data. Any changes to the data should be documented. Lock the updated data base with error free data. Slide 18: ADVERSE EVENT FORM AE 1.1 AE.CONDATE is blank, is a future date, or is before SCREEN.CONDATE for this PN. Message: “Q - Date of visit is either missing, is later than the date of data entry, is before this participant’s screening date, or is otherwise invalid.” 1.2 AE.PN = AE.PN, AE.CONDATE = AE.CONDATE, AE.AEDGLO = AE.AEDGLO, and AE.AEONDATE = AE.AEONDATE of previously loaded AE form. Message: “Q - Duplicate AE form for this participant.” Slide 19: Analysis of data Import data from data base Into SAS Create data sets Analysis as per client requirements Back Ups CRF’s are scanned soon after receipt in the CRF Tracking System and archived electronically as backup into an image database Benefits:- Effortless access to the CRF images It provides a single source for the most up to date copy of the CRF Ensures that the original entries were not overwritten during clinical CRF / data review Data Archiving : Data Archiving Keep a record Secure Stable eg; no floods Fire protected Pest control Packaging Security/Confidentiality : Security/Confidentiality Keep identifying data (name, SSN, MRN) in a separate table. Link rest of DB to this table via a Subject ID that has no meaning external to the DB. Restrict access to identifying data. Password protect at both OS and application levels. Mission of CDM : Mission of CDM Consistency Accuracy Validity Archiving Slide 27: 198.5 N Slide 28: 198.5 N INCORRECT DATA Slide 29: 198.5 N MISSING VALUES Slide 30: SRNO: 01 SID: A12 DEMOGRAPHIC DETAILS OF THE SUBJECT 1. SEX VALUES IS EITHER ‘M’ OR ‘F’. 2. PULSE VALUE IS NOT MENTIONED. SRNO: 03 SID: A25 DEMOGRAPHIC DETAILS OF THE SUBJECT 1. EXTREME VALUE HAS BEEN MENTIONED FOR TEMPERATURE. 2. SBP VALUE IS NOT MENTIONED. You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.