- This topic has 26 replies, 15 voices, and was last updated 2 years, 7 months ago by
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I’m working as a CRA. My main responsibility regarding clinical trials is data quality assurance (monitoring of clinical trial conducting). I have no direct experience in designing eCRF or databases involving activity.
Actually, I’m still quite new in this field, I sometimes missed the critical points during the on-site study visits. So, If I have the chance to go back I would concentrate more on monitoring activities and focus on preparing for monitoring as well.-
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I was considering to be a CRA at some point. I deem it as challenging because you will have to perform side-by-side check on protocol vs what has been or had been done – if they were following the protocol or deviations. I do enjoy performing quality check, but it must have been a headache when there are deviations!
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I don’t have any experience on clinical data. I have done only basic research. The logically order of the steps are quite similar. There are some steps that are in both clinical and basic research, but some are not.
The similar ones are
-Protocol discussion and data design would be the same when we plan and design the experiment out. What is the control? What is the method or technique to use?
-Data entry and processing would be equal when we are conducting the experiment and collect the results.
-QC and QA would be the same as when we analyse the results data. We focus on the control and see how it shows the results. If we are testing some drug or reagent at different concentrations, we should see some constant trend or no change. But definitely not, up and down results. This step in basic research we will do it together with data manipulation and analysis.
-Finally, we present our results same as data report.In basic research we are not concern much about database lock/security. We depends on the security of our work place only. We protect our data and properties from people outside.
Another thing that I think we less concern in basic research is archiving and backing up the data. I should be concerned about it more from now on.
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Most of my experience in the data management workflow has been with the data manipulation and analysis step. My experience with this step comes from my role as an engineer analyzing server logs to determine the root cause of an error. I manipulated the variables in the query to best fit the parameters of the error investigation. I then analyzed the results to deduce the possible root cause of the error.
I don’t have experience with the two ends of the data management workflow. As a technical-level engineer, I don’t have experience with the conception and creation phase of data management nor with the data entry phase. Since I acted immediately on the data, I don’t have any experience with the data reporting and archiving phases.
If I could go back, I would have tried to get more experience with the data entry and processing as well as the data validation and quality control steps. I would have been a more effective diagnostician if I had more familiarity with how the database stored and processed the specific data points, as I often struggled to write effective queries.
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It is important for the database programmer to understand the clinical data management process. I think a programmer (software engineer) view the database differently from clinical data manager. I sometimes find that the data collection program developed solely by an engineer has backend database that is difficult to utilize in terms of data analysis.
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I have not attended the clinical research before, I just performed some researches in biological and prevention fields. The data were very messy when we collected them. It was because we did not have the procedure for whole the research. Each person, each site collected based on their designs. That’s why I really like to attend this course to have a broad view about data management. In my opinion 2 steps are very important: procedure design and data QC, QA to make sure data’s accurate
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I do not have experience on being an associate for clinical trial. The nearest experience I had was on protocol development for a systematic review on a certain cancer drug. Basing on the steps, it touches mainly on the “Protocol Discussion” part were it was a collaborative process among stakeholders (eg, experts from various medical societies, pharmaceutical industry) as we aim to capture the needed variables and outcomes when we start our assessment.
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I would develop a well-written data management plan, particularly for metadata documentation. I am currently a bit struggling to code the questions and answers for analysis purposes because I did not plan and create the metadata beforehand.
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I worked as a pharmacovigilance specialist before dealing with adverse events and pharmacovigilance data systems. I am most involved in project implementation and closer parts, such as data entry, QC, data structure, study report, and SAE reconciliation. I do not have experience in project initiation, especially for CRF, DMP, and database setup and programming. If you have a chance to go back, I think I should have done better in data structure coding/management to prevent any messy data and easy to analyze later.
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In my experience, there are some steps in initial data management that I already did such as protocol discussion, data design, data acquisition, and database access control. However, there are many important steps that I did not perform such as data management plan development, edit check programming, data entry screen test, and CRF completion training. During the study conduct, I also missed the important steps of data validation and data quality control which cause the invalid and lots of missing data for further analysis. For the project closure, I did not know about quality assurance before and data standards or coding are also neglected.
If I have a chance to go back to improve the project, I think the protocol discussion and data design should be more consolidated than the previous. The data management plan should be established and should be well written. The investigator meeting and training are also important and should be done to arrange the understanding of all investigators. The data validation and data quality control are also the cornerstone of data entry and should be revised. The data quality assurance, data standard or coding also should be used to improve my future research project.
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Since I’m working as a Clinical Data Associate, I have to complete all the steps of data management workflow. However, some point that I’ve missed and need to emphasize more is about developing data management plan. I should make the DMP to be the best practice and also develop the DMP template for my organization to ensure how well the data is handled and organized throughout the entire project.
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I enjoy reading your experience sharing and your thought to improve your previous process. I think before we can perform things that is considered “best practice”, we need to have some previous experience of “bad practice” first. The important thing is that you must be able to recognize things that are “Bad practice”, then trying to improve them.
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Based on my experience in data collection and data management for routine surveillance activities for malaria programs, the steps that have been taken include:
1. Protocol discussion, this is carried out with the person in charge of each division, such as laboratories, case management, discussions are held to find out the flow and work processes in the laboratory, treatment, monitoring of treatment etc.
2. Data design (Variables/ Data workflow), determines important and useful variables for monitoring and evaluation
3. Data acquisition, to determine the method of data collection, whether offline or online
4. CRF Development
5. Data Management plan development, not well written
6. Databased access control, carried out to provide authorization at every level of admin and superuser
7. Databased setup and edit check programming, have been done but edit check type has not been done
8. Data entry screen test, done
9. Investigator meeting/ CRF Completion Training, done
10 Data entry and processing, already done
11. Data validation and quality control have been carried out.If I can repeat then the step that will be done is edit check to check the consistency and range of the data
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I have some experience in data designs in performing screening visits and follow up visits. Data management workflow includes protocol discussion, DMP, Data quality control (QC) and many others that i need to improve .
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I had assisted CRA performing eTMF documents uploading and archiving during the on-going until the study close out. To do this process well, one should have basic knowledge of the trial, be logical and attention to details to be able to re-check the completion, accuracy and timeliness the documents filed in eTMF. Also, making sure that the identifiable information of the subjects must not be showed on the documents processed to eTMF.
Steps I should have improved to my project: the clear communication among the team should have been put in place to emphasize on the timeliness of the documents collection so that the document filing in eTMF could be performed routinely and on a timely manner and to prevent overwhelming the tasks towards to study closure. -
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From my experience, I haven’t done any project initiation from scratch especially Clinical research.
I just have experience to analyse and manage data it after it was collected.But If I had a chance to improve some steps of data collection, I would pay the most attention to the early phase such as protocol discussion and data design because many project that I involved with usually wasn’t designed well enough to be utilized to reach the goal of research.
Many data just was collected for non specific purposes and in different standard of recording. Hence, in my opinion, the early stage is the most important stage to be focused on.
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