PHASES OF CLINICAL DATA. MANAGEMENT.pptx

Clinical Data Management (CDM) refers to the process of collecting, cleaning, and managing clinical trial data in compliance with regulatory standards.
Purpose: To ensure high-quality, reliable data that supports the evaluation of drug efficacy and safety.
Brief overview of the phases involved in CDM:
START UP PHASE
CONDUCT PHASE
CLOSE OUT PHASE

1. PHASES OF CLINICAL DATA MANAGEMENT(CDM) Ensuring Data Accuracy and Compliance in Clinical Trials Presented by Zamran Khan

2. Introduction to Clinical Data Management Clinical Data Management (CDM) refers to the process of collecting, cleaning, and managing clinical trial data in compliance with regulatory standards. Purpose: To ensure high-quality, reliable data that supports the evaluation of drug efficacy and safety.

3. Importance of Clinical Data Management Ensures data accuracy and integrity. Facilitates compliance with regulatory requirements. Enables timely and efficient decision-making. Supports new drug approvals and treatments.

4. PHASES OF CLINICAL DATA MANAGEMENT Brief overview of the phases involved in CDM: START UP PHASE CONDUCT PHASE CLOSE OUT PHASE

5. START-UP PHASE

6. The study start-up phase is the foundation of the clinical data management process, where all systems and processes are planned and set up to ensure the efficient handling of trial data. This phase focuses on defining how data will be collected and processed throughout the clinical trial. Protocol Review: The protocol is the blueprint for the clinical trial, outlining the study's objectives, design, data collection points, patient populations, and endpoints. The Data Management team carefully reviews the protocol to understand the data collection requirements and to design systems that align with the study's goals. The review ensures the protocol specifies what data is to be collected, how often, and in what format. Example : In a clinical trial testing a new diabetes medication, the protocol would include how frequently patients’ blood sugar levels should be measured, what other clinical data should be collected (e.g., weight, diet, insulin dosage), and the timelines for assessments.

7. CASE REPORT FORM (CRF) DESIGN: The Case Report Form (CRF), whether paper-based or electronic (eCRF), is the primary tool for collecting data during the trial. It is designed to capture all required data points as outlined in the protocol. The CRF design must be clear, intuitive, and standardized to reduce data entry errors. It should also ensure that data collected is consistent and complete. Often, a CRF annotation is developed, showing how each CRF field corresponds to the database structure. Example: In a heart failure clinical trial, the CRF would be designed to capture patient information such as blood pressure, heart rate, ejection fraction from echocardiograms, and any reported side effects. A Case Report Form (CRF) is designed by the CDM team, as this is the first step in translating the protocol-specific activities into data being generated. The data fields should be clearly defined and be consistent throughout. The type of data to be entered should be evident from the CRF. For example, if weight has to be captured in two decimal places, the data entry field should have two data boxes placed after the decimal as shown in Figure 1. Similarly, the units in which measurements have to be made should also be mentioned next to the data field. The CRF should be concise, self-explanatory, and user-friendly (unless you are the one entering data into the CRF). Along with the CRF, the filling instructions (called CRF Completion Guidelines) should also be provided to study investigators for error-free data acquisition. CRF annotation is done wherein the variable is named according to the SDTMIG or the conventions followed internally. Annotations are coded terms used in CDM tools to indicate the variables in the study. An example of an annotated CRF is provided in Figure 1. In questions with discrete value options (like the variable gender having values male and female as responses), all possible options will be coded appropriately.

8. Annotated sample of a Case Report Form (CRF). Annotations are entered in coloured text in this figure to differentiate from the CRF questions. DCM = Data collection module, DVG = Discrete value group, YNNA [S1] = Yes, No = Not applicable [subset 1], C = Character, N = Numerical, DT = Date format. For E xample, BRTHDTC [DT] indicates date of birth in the date format Figure 1

9. There are 2 types of CRF ( I.e ) paper and electronic CRF..

10. Database Design and Build A clinical trial database(DB) is built to store all the collected data securely and efficiently. This database must be capable of handling large amounts of clinical data and should allow for easy data entry, querying, and cleaning. The database structure must align with the CRF and protocol, and it should be designed to allow for accurate data capture, data validation checks, and analysis. The database may also incorporate tools for handling queries, flagging discrepancies, and generating reports. Database designing is done by Database Programmer and EDC Designer. Key points like Field length, dynamic forms, acceptable range, access level management will be designed as per the study requirement. CRF annotation plays a vital role in assigning the variables for each field. After Database designing is successful, DB Programmer will implement the conditions as per Data Validation Specification (DVS) ) for queries to fire for out of range conditions. Example : In a cancer immunotherapy trial, the database would store tumor size data, patient demographics, adverse events, laboratory results (e.g., biomarkers), and imaging data. The database would be built to allow for integration of various data types (numeric, text, image).

11. Data Management Plan (DMP): A Data Management Plan (DMP) is created to outline how data will be managed, including collection, processing, validation, and reporting. This document is essential for ensuring standard operating procedures (SOPs) are followed. The DMP provides a clear roadmap for all data management processes, including timelines, roles and responsibilities, data validation procedures, query management, and database locking processes. The DMP ensures that the trial follows Good Clinical Practice (GCP) guidelines and complies with regulatory standards. Example: In a multinational clinical trial for a new asthma medication, the DMP outlines how data from various sites will be collected through an Electronic Data Capture (EDC) system, how missing data will be handled, and how the database will be locked after query resolution.

12. Activities : Drafting the DMP, which includes data collection methods, validation checks, and data cleaning processes. Defines roles and responsibilities of the data management team. Establish procedures for handling missing data, queries, and discrepancies. Training : Investigators are trained on entering data into the EDC system and how to handle protocol deviations or adverse event reporting.

13. EDIT CHECKS: Edit checks are automatic warnings or notices that are generated by a database, CDMS, or other data entry application. They are triggered when data is inconsistent, missing, out of range, unexpected, redundant, incompatible, or otherwise discrepant with other data or study parameters. User Acceptance Testing (UAT): User Acceptance Testing (UAT) is the process were edit checks with specific conditions as per protocol is created by the Clinical Data Analyst (CDA) and Clinical Database Programmer (CDP) creates those condition in the Database to fire query for the conditions. Data Validation Specification (DVS) is the document were CDA writes all the edit checks and CDP modifies the EDC as per DVS. E.g.: As per protocol, Age Inclusion is 18 to 55 years. When the data is captured as below 18 or above 55, Query fires with Query Text as “As per protocol, Age Inclusion is 18 to 55 years, please confirm”

14. Sample Edit Check Specification Table

15. CRF Design and Edit Check Planning What Happens : The Case Report Form (CRF) is designed to capture all necessary data points defined by the clinical trial protocol. During the design process, data management teams identify potential data entry issues and discrepancies that may arise and plan corresponding edit checks. Edit Check Setup : Teams identify fields in the CRF where validation rules are necessary, such as ensuring numeric data falls within a valid range or that specific fields are mandatory. Example : If the protocol specifies that patient age must be between 18 and 65, an edit check will be designed to flag any ages outside this range. Types of Edit Checks Defined : Range checks : For example, systolic blood pressure should be between 90 mmHg and 200 mmHg. Consistency checks : Ensure that related data points, like gender and pregnancy status, are consistent. Date checks : Ensure that event dates (e.g., visit dates, start and stop dates) are logical. Example: The study protocol defines that all patients must have a baseline visit before receiving the study treatment. Edit checks are set to ensure no treatment data is entered before the baseline visit.

16. Electronic Data Capture (EDC) System Setup What Happens : An Electronic Data Capture (EDC) system is chosen, and edit checks are programmed into the system. This includes incorporating predefined validation rules into the system so that they trigger automatically during data entry. Edit Check Setup in EDC : Validation rules are built into the EDC system to ensure real-time data quality. Automatic queries are generated when discrepancies or errors are identified. Example : In a diabetes clinical trial, if the entered blood glucose level is outside the acceptable range (e.g., less than 70 mg/dL or more than 140 mg/dL), the EDC system will trigger a query to the investigator. DB is activated

17. Conduct phase

18. Conduct phase is the longest and most critical phase were Data capture, Data Cleaning, Data reconciliation, Medical Coding and Data Validation takes place with regular evaluation of data known as Interim Analysis along with documentations such as Query Management Form, Revision Request Form, Post Production Changes. Data Collection: Data is collected by investigators at clinical trial sites, entered into Case Report Forms (CRFs), and then submitted electronically via the Electronic Data Capture (EDC) system. This data includes patient demographics, medical histories, lab results, adverse events, and treatment information. Key Considerations : Data should be entered promptly to avoid delays and maintain real-time monitoring of the trial. Data entry must follow standardized formats and procedures outlined in the Data Management Plan (DMP). Example : A clinical trial testing a new hypertension drug collects data on patient blood pressure readings at each follow-up visit. This data is entered into the EDC system, and the system automatically checks that the readings fall within a specified range for each patient.

19. Methods :Electronic Data Capture (EDC) Paper CRFs Direct data entry or upload (e.g., patient-reported outcomes) Key Considerations : Real-time or periodic data entry Minimizing errors during data entry Site monitoring for compliance Electronic Data Capture (EDC): What It Is : An Electronic Data Capture (EDC) system is the most commonly used tool for data collection in modern clinical trials. It is a web-based system where clinical site staff can directly enter data from Case Report Forms (CRFs). These systems provide real-time data validation, edit checks, and query management, ensuring data is clean and accurate as it is entered.

20. How It Works : Site staff log into the EDC system to input patient data, including medical history, treatment details, lab results, and adverse event information. Edit checks are applied immediately, flagging any inconsistencies or missing fields. Advantages : Reduces manual data entry errors. Facilitates real-time monitoring and validation. Centralizes data, making it easily accessible for study monitors and data managers. Example: In a clinical trial for a diabetes drug, site staff enter patients' fasting blood glucose levels into the EDC system after each visit. If a value exceeds the pre-specified range (e.g., over 140 mg/dL), an automatic query is raised, prompting the site staff to verify the entry.

21. Paper CRFs: What is a Paper CRF? Paper CRF is a physical, paper-based form used by investigators or clinical site staff to collect data from clinical trial participants. Each CRF contains specific fields that correspond to data points required by the study protocol, such as demographic data, lab results, adverse events, and treatment outcomes. Once all the data in Paper CRF is filled, Site scan the CRF and sent to Data Management Team to capture the data in EDC along with Tracking Sheet. Tracking Sheet or Transmittal Sheet is a document were the Site mention How many Pages of Paper CRF are scanned for each patient and sent to Data management team to avoid missing pages. Once the Data Management Team receive the Scanned CRFs and Tracking sheet, Data Management Team verifies does Data Management Team received all the CRF pages as per mentioned in Tracking sheet. Once Tracking sheet matches, Data Management Team sign it and send back to Clinical Site. After this process, Data Entry Team captures the data present in scanned paper CRF to EDC (Electronic Data Capture) with the help of Data Entry Guidelines (DEG) Example : In a clinical trial for a new diabetes medication, the CRF may include fields for recording blood sugar levels, insulin use, and any side effects like hypoglycemia.

22. Direct Data Entry (DDE): is a method where data is entered directly into the system without using a traditional CRF. This is often used in studies where electronic devices are integrated with the data capture system. How It Works: Site personnel or the patient themselves enter data directly into a system via a tablet, mobile device, or computer. This data is captured electronically in real-time, without the need for transcribing from paper to digital format. Advantages:   Reduces data entry errors since no transcription is required.   Real-time data capture with immediate access for data managers.   Faster data availability for review and analysis. Example: In a clinical trial for an asthma drug, patients use a tablet to enter their daily symptoms and medication use. This data is transmitted directly to the central trial database, where it can be reviewed by investigators and data managers in real-time.

23. Data Validation and Cleaning What Happens : Data validation checks, including edit checks , are applied to the entered data to ensure it meets predefined standards for accuracy, consistency, and completeness. These checks help identify errors, inconsistencies, or missing data that need to be resolved. Edit Checks and Validation : Automated rules flag entries that deviate from the expected format, such as out-of-range values or inconsistent data points (e.g., a patient’s birthdate indicating they are too old to meet the study’s inclusion criteria). Data Cleaning : The process of identifying and correcting errors, ensuring that all discrepancies, missing fields, or incorrect entries are addressed. Example : During data entry, a patient’s weight is recorded as 600 kg, which is beyond the biologically plausible range. An edit check flags this as a potential error, and a query is generated, prompting the site to review and correct the entry.

24. Discrepancy Management: What is Discrepancy Management? Discrepancy management involves identifying, tracking, resolving, and documenting data inconsistencies, errors, or omissions that occur during the data collection and entry process in clinical trials. These discrepancies can arise from various sources, including data entry errors, incomplete information, deviations from the protocol, or unexpected outliers. Types of Discrepancies Missing Data : Definition : Data fields that should have been completed but were left blank. Example : In a clinical trial, a patient's lab result for a key biomarker is not recorded in the Case Report Form (CRF). Inconsistent Data : Definition : Data that contradicts other information in the trial database or violates logical relationships between variables. Example : A patient’s date of birth suggests they are 25 years old, but their age is recorded as 60 in the same dataset.

25. Outliers : Definition : Values that fall outside the expected range based on the study protocol or statistical expectations. Example : A patient's blood pressure reading is recorded as 300/180, which is unusually high and requires investigation. Protocol Deviations : Definition : Data that does not conform to the study protocol’s requirements, such as tests done outside the required visit window. Example : A follow-up visit is conducted 45 days after the previous visit, but the protocol specifies a maximum interval of 30 days. Data Entry Errors : Definition : Mistakes made during the manual entry of data into the electronic data capture (EDC) system. Example : A weight of 180 kg is entered instead of 80 kg due to a typo.

26. Discrepancy Management Process Data Entry and Automatic Edit Checks : During data entry, automatic edit checks in the EDC system help to identify potential discrepancies in real-time. These checks are pre-programmed rules based on the study protocol and expected data ranges. Example : If the study expects a patient’s body temperature to be between 35°C and 40°C, and a value of 42°C is entered, the system will flag this as a discrepancy and prompt a query. Query Generation : When a discrepancy is detected (either through an automated check or manual review), a query is generated. This query is sent to the clinical site for clarification or correction. Types of Queries : Automatic Queries : Raised automatically by the EDC system when data violates preset rules. Manual Queries : Raised by data managers or monitors when they review the data and identify discrepancies not caught by automatic checks.

27. Query Resolution : Once a query is raised, site staff are responsible for reviewing the flagged data, investigating the issue, and providing clarification or correction. Resolution Process : Correction of Errors : If the issue is due to a simple data entry error, the site can correct the value directly in the EDC system. Clarification : If the data is correct but appears to be an outlier or violates a protocol rule, the site can provide an explanation or supporting information. Example : If a patient's weight is recorded as 150 kg, which seems unusually high, the site staff may confirm that the value is accurate based on the patient’s medical history or recheck the measurement. Data Review and Reconciliation : After the query is resolved, data managers review the updated data and ensure that all discrepancies are appropriately addressed and documented. During the reconciliation process , data from different sources (e.g., laboratory results, patient diaries, medical records) are compared to ensure consistency.

28. Monitoring and Reporting : Throughout the conduct phase, the data management team monitors the overall progress of discrepancy management. They track the number of open, pending, and resolved queries, ensuring timely resolution. Example : A monthly report may be generated to assess how many discrepancies have been raised, resolved, or are still open, helping trial managers identify any recurring issues or bottlenecks. Documentation and Audit Trail : All discrepancy management activities are documented to maintain an audit trail. This is important for regulatory purposes and ensures transparency. The EDC system typically logs every action taken to resolve discrepancies, including query generation, response, resolution, and any data changes.

29. The four types of queries are,

30. Medical coding Medical coding is a critical process in the conduct phase of Clinical Data Management (CDM), used to standardize clinical trial data, especially when dealing with adverse events, medications, and medical history. This ensures that all clinical data is uniform, enabling consistent data analysis and reporting across different sites, studies, and regulatory authorities. What is Medical Coding in Clinical Trials? Medical coding is the process of translating verbatim terms recorded by investigators (e.g., descriptions of adverse events, diagnoses, or medications) into standardized codes using globally accepted dictionaries or coding systems. These codes make it easier to categorize and analyze clinical trial data systematically. Commonly used medical coding dictionaries include: MedDRA (Medical Dictionary for Regulatory Activities) : Used for coding adverse events, medical conditions, and procedures. WHO-DD (World Health Organization Drug Dictionary) : Used for coding medications and therapeutic products.

31. Safety Data Reconciliation Safety data reconciliation is an essential process during the conduct phase of clinical trials, ensuring that safety-related information is consistent across different data sources. This process is particularly important for clinical trials where safety data, such as adverse events (AEs) or serious adverse events (SAEs), are captured and reported in multiple systems, such as the clinical trial database and the pharmacovigilance (PV) system . The main goal of safety data reconciliation is to ensure that the safety data reported in the Clinical Data Management System (CDMS) aligns with the data in the Pharmacovigilance (PV) database and that no discrepancies exist between the two. This ensures regulatory compliance and accurate safety reporting, which is critical for the protection of trial participants and the evaluation of the investigational product's safety profile. Key Terms Clinical Data Management System (CDMS) : This system holds the data collected during the trial, including adverse events (AEs) reported by investigators. Pharmacovigilance Database : This system stores all safety-related information, including SAEs and other safety events that may need to be reported to regulatory authorities. Serious Adverse Events (SAEs) : Adverse events that result in death, hospitalization, disability, or are life-threatening.

32. Why Safety Data Reconciliation is Important? Ensures Data Consistency : Since AEs and SAEs are reported in both the CDMS and the pharmacovigilance database, reconciling the two ensures that there are no discrepancies in the data. Regulatory Compliance : Regulatory authorities like the FDA and EMA (European Medicines Agency)require accurate reporting of safety data. Any mismatch in the data could lead to compliance issues or delayed approvals. Data Integrity : Accurate reconciliation helps maintain the integrity of the clinical trial data and ensures reliable safety assessments. Improved Safety Signal Detection : Consistent safety data across databases helps sponsors and investigators detect potential safety signals or emerging trends that need to be addressed promptly.

33. The safety data reconciliation process typically involves the following steps: 1. Data Collection During the conduct phase, adverse events and serious adverse events are reported by investigators at clinical sites. These events are recorded in two systems: The CDMS , where all trial data (including AEs) is collected. The Pharmacovigilance Database , where SAEs and other safety-related data are captured for regulatory reporting. 2. Identification of Discrepancies The CDM team compares the data in the CDMS with the data in the pharmacovigilance database to identify any discrepancies . Discrepancies could arise due to: Missing Data : An SAE reported in the pharmacovigilance system is not found in the CDMS or vice versa. Inconsistent Data : Differences in how the adverse event is described, including severity, onset date, or outcome. Duplicated Data : The same adverse event reported twice in one system but not reflected correctly in the other.

34. 4. Documentation and Reporting All actions taken to reconcile discrepancies are documented in an audit trail . This is important for regulatory compliance and ensuring transparency in how discrepancies were resolved. After reconciliation, any updated data is included in regulatory submissions, such as clinical study reports (CSRs) or safety update reports to authorities. 5. Ongoing Monitoring Safety data reconciliation is not a one-time event. It is typically conducted at regular intervals (e.g., monthly or quarterly) throughout the clinical trial to ensure continuous alignment between the CDMS and pharmacovigilance database.

35. Scenario : A clinical trial is testing a new drug for diabetes, and adverse events are being reported by investigators at various trial sites. An SAE (hospitalization due to severe hypoglycemia) is reported during a patient visit. Data Collection : The investigator records the SAE in the CDMS as "hospitalization due to low blood sugar." The same SAE is reported to the pharmacovigilance team , which enters the event into their safety database as "hospitalization due to severe hypoglycemia." Identification of Discrepancies : During reconciliation, the data manager notices that in the CDMS , the SAE is described as "low blood sugar," whereas in the pharmacovigilance system, it is recorded as "severe hypoglycemia." Additionally, the onset date of the SAE differs between the two systems by two days.

36. Reconciliation : The clinical data management team contacts the site for clarification on the discrepancy. The site confirms that the correct medical term for the event should be "severe hypoglycemia" and that the onset date in the CDMS was entered incorrectly. The CDMS is updated to reflect the correct term and onset date, ensuring alignment with the pharmacovigilance system. Documentation and Reporting : The reconciliation process, including the steps taken to resolve the discrepancies, is documented in both the CDMS and pharmacovigilance system audit trails. The updated safety data is included in the next safety update report submitted to regulatory authorities. Ongoing Monitoring : The CDM and pharmacovigilance teams continue to perform regular reconciliations throughout the trial, ensuring all SAEs and other safety events are consistently recorded in both systems.

37. Improves Data Quality : Reconciliation ensures that all safety data is complete, accurate, and consistent between systems. Reduces Risk of Errors : Identifying and resolving discrepancies early reduces the risk of errors in regulatory submissions, helping to avoid costly delays in trial approvals. Supports Regulatory Compliance : Proper reconciliation ensures that the sponsor is compliant with regulatory requirements for safety reporting. Enhances Patient Safety : Accurate and consistent safety data helps sponsors and regulatory authorities identify safety issues early, allowing for timely action to protect patients.

38. CLOSE OUT PHASE

39. The close-out phase is the final step in the Clinical Data Management (CDM) process and occurs after the clinical trial's conduct phase is completed. This phase focuses on ensuring that all clinical trial data has been reviewed, validated, cleaned, and locked for final analysis and regulatory submission. Proper execution of this phase is essential for ensuring data accuracy and completeness, as well as compliance with regulatory requirements. Final Data Cleaning Final data cleaning involves ensuring that all queries and discrepancies raised during the conduct phase have been resolved. This step is essential for preparing the data for final analysis. Activity : The CDM team performs a final review of the data to ensure there are no unresolved discrepancies, missing data, or incomplete records. Example : In a clinical trial testing a new vaccine, the data managers ensure that all adverse events reported during the study are properly coded, and all missing or incomplete patient data is filled in based on site follow-ups. Any last-minute queries raised by the data validation tools are addressed and resolved.

40. Database lock is the final process in Clinical Data Management. After all the queries are actioned and all outstanding issues are resolved, The Final Clean Data is frozen either manually or by script to ensure the data is not edited further which means, post Freezing, data will be in Read-only mode. Once all the data are frozen, The Database lock approval is sent to stakeholder and sponsors. Post- approval, The Database will be successfully locked and is always considered as a milestone achieved by the Data Management Team. Post Database lock, the data will be extracted by Statistical programmers for analysis and Data management team completes all further documentation and proceed for Archival. This is a procedure which is done at the end of clinical trial after the last query is resolved & prior to DB locking/Freezing : This procedure ensures the following points are met: Data is complete i.e., No missing data Data is consistent Data is accurate Data is reliable

41. Purpose of Database Lock The main objective of the database lock is to: Ensure Data Integrity : After the lock, the data is frozen, ensuring no changes can be made, which prevents errors or bias in the final analysis. Prepare for Final Analysis : The locked data is ready for biostatistical analysis, providing reliable and validated results. Regulatory Compliance : Regulatory bodies such as the FDA or EMA require the data to be locked before submission to demonstrate that no further alterations can take place. Finalize Data for Reporting : Once locked, the data is used to create clinical study reports (CSRs) and other documentation required for regulatory submission and drug approval processes. Example: In a clinical trial studying a new cancer drug, after resolving all data queries and discrepancies, the data management team locks the database to prevent any further alterations. This step marks the point at which the data becomes ready for statistical analysis.

42. Why is Database Lock Important? Ensures Data Integrity : Once the database is locked, no changes can be made, ensuring that the data used for analysis is consistent and reliable. Regulatory Compliance : Regulatory authorities like the FDA and EMA require the database to be locked before data is submitted for review. Locking the database demonstrates that the data has been thoroughly reviewed and is ready for final analysis. Prevents Bias : The database lock ensures that the final analysis is based on fixed data, preventing any intentional or unintentional alterations that could introduce bias. Enhances Data Security : By locking the database, the trial data is protected from further modifications, enhancing the security and integrity of the data.

43. Database Lock Authorization After final validation, the CDM team, along with stakeholders (such as the sponsor, biostatisticians, and clinical teams), conduct a final review of the data. Upon reaching a consensus that the data is ready, the database is formally locked. Activity : A meeting is held to review the final data and obtain agreement from all stakeholders. The database is locked when all parties approve. Example : In a trial for a new vaccine, the data management team, sponsor, and clinical leads review the data and agree that it is ready for the final analysis. Once everyone approves, the database is locked. Post-Lock Activities After the database is locked, the data becomes fixed for statistical analysis and regulatory reporting. Any further changes can only be made through a formal unlock request, which is typically avoided unless critical corrections are needed. Activity : The locked data is used to perform statistical analyses and generate clinical study reports (CSRs), which are submitted to regulatory authorities. Example : After locking the database for a clinical trial of a new hypertension drug, the biostatistics team begins analyzing the data, and the results are included in a clinical study report for submission to the FDA.

44. Each phase of CDM is critical for ensuring the accuracy, reliability, and integrity of clinical trial data. From designing effective data collection tools to cleaning and locking the database, each phase plays a vital role in ensuring that clinical trial data meets the rigorous standards set by regulatory bodies, ultimately leading to safer and more effective treatments reaching patients.

45. The Study Data Tabulation Model Implementation Guide (SDTMIG) is a guide for organizing, structuring, and formatting clinical trial data for submission to regulatory authorities like the US Food and Drug Administration (FDA). 

46. Data validation specification(DVS) in clinical data management refers to the detailed documentation that outlines the tests and checks to ensure data quality and integrity according to predefined protocol specifications. This process typically involves structured approaches to verify the accuracy, completeness, and consistency of collected data, often using edit check programs to identify any discrepancies.

47. Data entry guidelines (DEGs) in clinical data management (CDM) define how data should be entered, formatted, and validated. These guidelines can vary based on the data's type, source, and purpose, as well as the expectations of the data users and stakeholders

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