Draft data management - #28
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@carriewright11 this is a draft of chapter 5 (with the updates we discussed in our last meeting). It is mostly complete, but is missing a conclusion and images. I can add these to the pull request but first wanted to make sure that this all looks good, because it is my first contribution to this course or any of the ITCR courses. I can brainstorm where to add images/what images to add but am open to any ideas that you or @kweav have, if you had anything in mind. |
| #### Open source options | ||
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| Open-source tools provide a cost-effective and flexible alternative to proprietary software for handling clinical data. These tools are often developed and maintained by vibrant communities and can be customized to fit specific research needs. | ||
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Possible figure here that open source software doesn't mean data won't be protected (but still have to do work to make sure data is protected)
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May be better later in the section (looks like line 74)
Co-authored-by: Kate Isaac <41767733+kweav@users.noreply.github.com>
…ical_Data_Management into draft_data_management
…o newer or less common tools
…can do basic repots but not advanced statistical modeling or analyses
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@carriewright11 this PR is ready for your review now! |
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Looks great! Had some minor comments
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| ### Testing data handling tools | ||
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| Before implementing data handling methods in an actual clinical trial, it is crucial to run tests using **dummy data**. This step ensures that the data handling process is robust, efficient, and free from errors across different conditions, without risking sensitive patient information. |
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lets link out to the synthetic data section ... also might be good to describe https://www.questionpro.com/blog/synthetic-data-vs-simulated-data/ - either here or there and clarify what dummy data is vs synthetic vs simulated
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I mentioned this in issue #45 about linking to the synthetic data section.
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| ### Data Management Plan | ||
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| A Data Management Plan (DMP) outlines how data will be handled during and after a research project. Typically created at the beginning of a project, it is stored in project documentation repositories or institutional databases. Often required by funding agencies, a DMP may include plans for testing with dummy data to ensure data integrity. The importance of a DMP lies in its role in planning and managing data throughout the project, ensuring that data is handled consistently and responsibly [[@DataManagement]](https://www.tableau.com/learn/articles/what-is-data-management). |
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Depending on what the updates were for the data management plans for NIH (i don't know how much it simplified it - but previously for grant funded research you had to have a plan) - we might want to reference our course and or the NIH links
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| Data Use Agreements (DUAs) define the terms and conditions for data sharing and use. Encountered before data is shared with external parties, they are stored in legal or administrative offices. DUAs are required when sharing data with external collaborators but are typically not directly related to dummy data. DUAs are important for protecting data privacy and ensuring that data is used appropriately [[@DataCompliance]](https://www.ibm.com/topics/data-compliance). | ||
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| ### Data Sharing Agreements |
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it would be nice to modify these a bit more to make it clear what the difference is between a DUA and DSA... https://casrai.org/guides/data-sharing-agreements-between-collaborators-and-institutions
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I see that the summary describes them in a more distinct way.
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| Honest brokers act as neutral intermediaries between the data source and researchers, typically regulated by Institutional Review Boards (IRBs) or equivalent bodies. They are responsible for de-identifying data to ensure that researchers cannot trace the data back to individual patients. Honest brokers must complete specific training, such as Collaborative Institutional Training Initiative (CITI) Research Ethics and HIPAA training, before accessing data. They play a crucial role in maintaining the confidentiality and integrity of clinical data. | ||
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| ## Documentation |
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could have an image that briefly describes DMP, SAP, SOP, DUA, and DSA
OTTR Check ResultsSummary
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Co-authored-by: Carrie Wright <23014755+carriewright11@users.noreply.github.com>
Co-authored-by: Carrie Wright <23014755+carriewright11@users.noreply.github.com>
3 participants
Purpose/implementation Section
Chapter 5: clinical data management
What changes are being implemented in this Pull Request?
This pull request adds a draft of chapter 5: clinical data management.
What was your approach?
I transferred the content for this chapter from the Google Drive, editing and re-ordering as needed to make it fit into the framework of the course.
What GitHub issue does your pull request address?
No current issues.
Tell potential reviewers what kind of feedback you are soliciting.
I would like to get feedback on the overall structure of this chapter, as this is my first addition to this course. I would also appreciate any ideas of where to add images.
New Content Checklist
New content/chapter is in an Rmd file with this kind of format and headers.
New content/chapter contains Learning Objectives and are in the correct format.
Bookdown successfully re-renders and any new content files have been added to the _bookdown.yml.
Spell check runs successfully in Github actions style-n-check).
Any newly necessary packages that are needed have been added to the Dockerfile and image.
Images are in the correct format for rendering.
Every new image has alt text and is in a Google Slide.
Each slide is described in the notes of the slide so learners relying on a screen reader can access the content. See https://lastcallmedia.com/blog/accessible-comics for more guidance on this.
The color palette choices of the slide are contrasted in a way that is friendly to those with color vision deficiencies.
You can check this using Color Oracle.