Welcome clinical data management program using SAS. In this video we'll be discussing about clinical data management activities that is different clinical data management activity. So what are the different clinical data management activities first comes data collection next CRF tracking next data entry next data validation then discrepancy management, medical coding and database quality. So, let me explain you all individually each of the data management activities that is each of the clinical data management activities first is data collection. Data Collection is done using the CRF that may exist in the form of a paper or an electronic version. The traditional method is to employ paper CRS to collect the data responses, which are translated to the database by means of data entry done in house.

These paper CRS are filled up by the investigator according to the completion guidelines in the ecrf based CDM. The investigator or a design designer will be logging into the CDM system and entering the data directly at the site in ecrf method chances of error Less, and the resolution of discrepancies happens faster since pharmaceutical companies try to reduce the time taken for drug development processes. by enhancing the speed of processes involved. Many pharmaceutical companies are opting for ecrf options. Next is CRF. Tracking.

The entries made in the CRA will be monitored by the clinical research associate that the CRA for completeness and filled up CRS are retrieved and handed over to the CDF team. The CDM team will track the chief CRS and maintain their record. CRS subtract for missing pages in illegal data that is illegible data manually to assure that the data are not lost in case of missing or illegible data or clarification is obtained from the investigator and the issue is resolved. Next is data entry. Data Entry takes place according to the guidelines prepared along with the DMP. This is applicable only in the case of paper CRF retrieved from The site's usually double data entry is performed wherein the data is entered by two operators separately.

The second pass entry helps in verification and reconciliation by identifying the transcription errors and discrepancies caused by illegible data. Moreover, double data entry helps in getting a cleaner database compared to a single date and earlier studies have shown that double data entry ensures better consistency with paper CRF as denoted by a lesser error rate. Next comes the concept of data validation data validation is the process of testing the viability of data in accordance with the protocol specifications. Edit check programs are written to identify the distribution sees in the entered data which are embedded in the database to ensure data validate these programs are written according to the Logic Condition mentioned in the DVP. These edit check programs are initiated tested with dummy data containing discrepancies. discrepancy is defined as a data point that fails to Pass a validation check discrepancy may be due to inconsistent data, missing data, rain checks and deviations from the protocol in ecrf.

Based studies data validation process will be run frequently for identifying discrepancies these discrepancies will be resolved by investigators after logging into the system ongoing quality control of data processing is undertaken at regular intervals during the course of CDM. For example, if the inclusion criteria specified at the age of the patient should be between 18 to 65 years, both inclusive and edit program will be written for two conditions that is age less than 80 and greater than 65. If for any patient the condition becomes true or discrepancy will be generated. These discrepancies will be highlighted in the system and data verification forms can be generated. dcfs are documents containing queries pertaining to the discrepancies identified. Next comes the concept of discrepancy management discrepancy management.

This is also called query solution resolution discrepancy management includes reviewing discrepancies investigating the reason and resolving them with documentary proof or declaring them as a resolve. discrepancy management helps in cleaning the data and gathers enough evidence for the deviations observed in data. Almost all cdms have a discrepancy database where all discrepancies will be recorded and stored with auditor based on the types identified discrepancies are either flagged to the investigator for clarification or closed in house basis. We did corrections without sending VCF to the site. The most common sec s are obvious spelling errors for discrepancies that require clarification from the investigator DCs will be sent to the site the CDM tools help in the creation printing of DCs, investigators will write the resolution and explain the circumstances that led to the discrepancy in data when a resolution is provided by the investigator the same will be updated in the database in case of ecrf.

The investigator can access the district NC flashed to him and we'll be able to provide resolutions online. The CDF team reviews all discrepancies at regular intervals to ensure that they have been resolved the resolve details to currencies are recorded as closed. This means that those validation failures are no longer considered to be active and future data validation attempts on the same data will not create it is too busy for seeing data point. But closure of discrepancies is not always possible. In some cases, the investigator will not be able to provide a resolution for the discrepancy. Such discrepancies will be considered as irresolvable and will be updated in the discrepancy database.

Discrepancy management is the most critical activity in the CDM process. Being the vital activity in cleaning up the data most attention must be observed while handling the disturbances. Next comes the concept of medical coding medical coding helps in identifying and properly classifying the medical terminology associated with a clinical trial. For classification of events medical dictionaries available online are used. Technically this activity needs the knowledge of medical terminology understanding of disease entities, drugs used and the basic knowledge of the pathological processes involved. Functionally it also requires knowledge about the structure of electronic medical dictionaries and the hierarchy of classifications available in them.

Adverse events occurring during the study prior to an and concomitantly administered medications and free or causes to illnesses are coded using the available medical dictionaries commonly medical dictionary for regulatory activities is used for the coding of adverse events, as well as other illnesses and World Health Organization drug dictionary and enhanced is used for coding the medications these diction dictionaries contain the respective classifications of adverse events and drugs in classes. Other dictionaries are also available for use in data management some pharmaceutical companies utilise customers to dictionaries to suit their needs and meet their standard operating procedures. Medical Coding helps in classifying reported medical terms on the CRF two standard dictionary terms in order to achieve data consistency and avoid unnecessary duplication. For example, investigators may use different terms for the same adverse adverse event, but it is important to code all of them to a single standard code and maintain uniformity in the process.

The right coding and classification of adverse events and medication is crucial as an incorrect coding may lead to masking of safety issues or highlight the wrong safety concerns related to the truck next, and the last data management activity is database locking. After a proper quality check and assurance the final data validation is run if there are no discrepancies the SAS data sets a finalized in consultation with the statistics or Data Management Act. tivities should have been completed prior to database lock to ensure this pre launch checklist is used and completion of all activities is confirmed. This is done as the database cannot be changed in any manner after locking. Once the approval for locking is obtained from all stakeholders, the database is locked and cleaned it is extracted for statistical analysis generally no modification in the database is possible. But in case of a critical issue or for other important operational reasons, privileged users can modify the data even after the database is locked.

This however request proper documentation and an audit trail has to be maintained with sufficient justification for updating the lock database data extraction is done from the final database after lock. So this is the concept of database locking. There are certain roles which are considered as minimum requirements for a CMT. That is its data manager database programmer. designer, medical coder, clinical data coordinator quality control associate and data entry associate. So in this video, we'll be learning to hear in my next video, we'll be starting the practical session of clinical data management.

That is we will be starting with clinical data management using SAS. We'll be starting with SAS essentials first for now, let me end this video over here. Thank you Goodbye. see you for the next video.