Saranath

Good points! I can summarize from your answers that clinical data standards (such as CDISC) can
– assist data sharing and pooling data from different studies for further analyses.
– provide a standard language for researcher to understand and interpret the clinical data.
– provide trustable data and analysis outcomes.

Thank you all for sharing!

Good catch all! There are more points that are needed to be improved. Perhaps, you all can help adding those points, as it is over the deadline. I will provide the complete answer(Revised CRF) in week 5 module.

Yes, communication and timeline are very important for data management process.

Thanks! Hope that you can apply what we have learned in this course to improve your work.

Great! You have been through almost all data management steps. Having edit check programming will be very helpful to improved data quality.

In clinical trial- all AEs and SAEs events should be coded following standard system organ class (SOC) term- MedDRA standard coding. When you have standard coding- it would make it easier to sort, categorize.

That’s OK. How do you collect your lab data, using an excel sheet?

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.

Thank you everyone for sharing your experience. I could summarize the problems that you have faced as.

1. Data quality: unstructured data, incomplete data, inaccurate/inconsistent data.
2. Data collection method: Paper-based, interviewer, electronic (server downtime).
3. Protocol: unconsolidated protocol, questionnaire development.

Good catch! Waiting for others to participate.

Great!

I’m sure the EDC system that you use would have a concrete data backup and recovery plan. It would be much easier for the users (or CRA), if you have a good system to work with.

In fact, data management process can be applied to research or data collection outside the clinical trial research. Hope you can apply what we learn in this course to some of your works.

Great! Hope this course can help you improve data collection/management/analysis in your research.

Great! Hope this course can help you improve data collection/management/analysis in your research.

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.

Thanks for sharing! I can imagine how difficult the work is. Carefully designing the database is a very important step.

Thanks for sharing! Qualitative data collection is even harder to transcribe and analyze than quantitative data collection, but somehow it can give you an in-depth information that just a questionnaire may not be able to provide.

Thanks for sharing! Qualitative data collection is even harder to transcribe and analyze than quantitative data collection, but somehow it can give you an in-depth information that just a questionnaire may not be able to provide.