Recommendations for oganizational best practices to support data collectionThe following best practice recommendations are general data collection principles for use by organizations to ensure data collected are fit for purpose, complete, and of high quality.
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| Num | Best Practice Recommendation | Rationale | | 1 | Collect necessary data only. CRFs should focus on collecting only applicable data in alignment with regulatory needs. The protocol should clearly state which data will be collected in the study. | Usually, only applicable data collected in alignment with regulatory needs should be collected on the CRF, due to the cost and time associated with collecting and fully processing the data. However, some fields on a CRF may be present to support | EDC functionality andelectronic data capture (EDC) functionality and/or review and cleaning of data through automated edit checks. The protocol |
, statistical analysis plan (), when it is prepared in conjunction with the protocol |
, ) should be reviewed to ensure that the parameters needed for analysis are collected and can be easily analyzed. The statistician and/or principal investigator is responsible for confirming that the CRF collects all of the data necessary to support the analysis. | | 2 | CRF development should be a controlled, documented process that incorporates (as applicable): - Design
- Review
- Approval
- Versioning
- Printing
- Distributing CRFs
- Accounting for unused CRFs
CRF development should be controlled by standard operating procedures (SOPs) covering these topics, as well as |
site training. | A controlled process for developing CRFs will help ensure that CRFs comply with |
company applicable standards and processes. | | 3 | The CRF design process should include adequate review and approval steps, and each reviewer should be informed on the scope of the review they are expected to provide. The team that designs the data collection instruments for a study should be involved in the development of the protocol and should have appropriate expertise represented on the CRF design team, including the following: - Scientific experts should provide sufficient information to ensure
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clinical - data standards staff, subject matter experts, and
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clinical - data management staff understand the background, context, and scientific relevance of
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the efficacy and/or safety data- study data being collected.
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Clinical data - Data management, standards subject matter experts, and CRF designers should review the protocol to ensure that proposed data can be collected
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, - and should ensure that appropriate standards are used to develop the CRF.
- Statisticians should review the CRF against their planned analyses to make sure all required data will be collected in an appropriate form for those analyses.
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Clinical operations staff should - Operations experts should review the CRF to make sure the questions are unambiguous
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and that - , requested data can be collected, and for compatibility with common workflow and procedures.
- Programmers should review the CRF to ensure that the manner in which the data are collected on the CRF is consistent with relevant metadata standards.
- Regulatory experts should review the CRF for compliance with all applicable regulations.
- Data
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entry staff should - management experts should review the CRF to ensure that the data are collected in a form that can be entered accurately
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.Pharmacovigilance personnel should review to ensure appropriate data capture and process to support expedited reportingIdeally, the CRF should be developed in conjunction with the protocol (and the SAP, if available). All research-related data on the CRF should be addressed in the protocol to specify how and when it will be collected. | Reviewers from different functions increase the probability that the CRF will be easier to complete and support the assessment of |
safety and efficacy the product as defined in the protocol and SAP. The CRF design team should ensure that the data can be collected in a manner that is consistent with the implementer’s processes and is easy |
for the site | | 4 | Translations of CRFs into other languages should be done under a controlled process by experts who understand both the study questions and the language and culture for which the CRF is being translated. The translation should be a parallel process following the same set of steps with separate reviews and approvals by the appropriate experts. Translations may require author approval and a separate validation of the translated instrument. CRFs that are translated into other languages should follow the same development process as the original CRF to ensure the integrity of the data collected. Consideration of translation should be part of the CRF development process. Avoid the use of slang or other wording that would complicate or compromise translation into other languages. | Cultural and language issues should be addressed appropriately during the process of translating CRFs to ensure the CRF questions have consistent meaning across languages. | | 5 | Data that are collected on CRFs should be databased. For some fields, such as “Were there any Adverse Events?" the response of "Yes/No” may need to be databased but will not be included in |
the submission datatabulation datasets. Some fields, such as Investigator’s Signature, can be verified by the data entry staff, but an actual signature may not be databased unless there is an e-signature. | If certain data are not required in the CRF, but are needed to aid the investigator or monitor, those data should be recorded on a |
site worksheet (e.g., entry criteria worksheet, dose titration worksheet). All such |
site worksheets should be considered source documents or monitoring tools |
, at the site with the study files. | | 6 | Establish and use standardized CRFs. | Using data collection standards across studies saves time and money |
at every step of drug and device developmentacross tobacco product development steps. Using standards: - Reduces production time for CRF design and reduces review and approval time
- Reduces
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site - retraining and queries and improves compliance and data quality at first collection
- Facilitates efficient monitoring, reducing queries
- Improves the speed and quality of data entry due to familiarity with standards and reduces the training burden in-house
- Enables easy reuse and integration of data across studies and facilitates data mining and the production of integrated summaries
- Reduces the need for new
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clinical and statistical - programming with each new study
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