The ten commandments of translational research informatics

2.1.Commandment 1: Create a separate data management work package

When clinicians, biologists and other researchers come together in a translational research project, they often do not think about data management, data curation, data integration and the IT infrastructure, except for when it is already too late: the data sit on several computers scattered over different organizations, and nobody knows how to combine them and make sense of them. The solution: create a separate work package or work stream on data management. This work package can be thought of as a sub-project, which has its own goal (or even a list of milestones and deliverables) and has full-time equivalents (FTEs) and other financial resources allocated. Within this work package, a data management plan (DMP) will be created which describes exactly how the data management will take place (such a DMP is obligated nowadays in several funding programmes such as Horizon 2020 [17], and with good reason). Since this data management work package will have touchpoints with the other (data generating) work packages, the data management work package leader needs to be involved in all project meetings. It is also advised to create a separate work package on data analysis, which gets its input from the data management / data integration work package (Fig. 1).

Fig. 1.

A proposed work package (WP) structure for a translational research project.

2.2.Commandment 2: Reserve time and money for data entry

Investigators tasked with generating the hypotheses may not have the expertise to understand the complexities of processing and managing data that are not clean and ready for analysis. For example, they usually lack knowledge about an Electronic Data Capture (EDC) system such as OpenClinica [35], Castor EDC [6] or REDCap [16], as part of work package 1 in Fig. 1. This work is often assigned to trial nurses, who usually already have a high workload. The data entry work is on the bottom of their priority list, which can cause delays and even errors. Which is a large concern, because data quality is an important matter when it comes to data analysis [5]. Even when the data are extracted from an Electronic Health Record (EHR) and entered into the EDC system automatically, this needs to be checked by someone. The term “Garbage in, garbage out” (GIGO) comes from computer science, but applies to medical data as well [26]. The solution here is to reserve money to hire people who can do this job for a certain amount of hours per week. By spending relatively little money on data entry, one can save a lot of time and money by not having to redo analyses because of missing or erroneous data.

This commandment does not apply to just clinical data but to the other data types as well: imaging and digital pathology data often need to be annotated, biobank data usually need to be exported from a Laboratory Information Management System (LIMS), and raw genomics data need to be processed first before they can be used in an integrated database. All these steps need sufficient resources to make sure that they are executed correctly.

2.3.Commandment 3: Define all data fields up front together with the help of data analysis experts

Within the Prostate Cancer Molecular Medicine (PCMM [20]) project, we noticed after a few years that some information that was essential to answer certain research questions was not being collected in the electronic case report form (eCRF). A second eCRF needed to be constructed, which resulted in a lot of time being spent on going back to the patient’s entries in the hospital system and collecting the data, if they were there at all. We learned our lesson. Within the Movember Global Action Plan 3 (GAP3) project [21], all parties together created the Movember GAP3 codebook, which was an extensive list of data fields designed to answer all research questions that we could think of at the start of the project. The statisticians within the project were very much involved in the codebook creation, because they had the clearest insights on what was needed here. Besides the data field name, we stored the data type (integer, float, string, categorical, etc.) and the unit (days, years, cm, kg, kg/m², ng/ml, etc.), and (in case of a categorical data type) listed the categories (e.g. the TNM staging system). In case of a derived data field, the codebook explains how this data field is calculated. Examples here are data fields such as age, BMI and days since diagnosis. Because part of the Movember GAP3 data was retrospective, we created some data model mapping scripts [23] to map the existing data to this codebook, as well as some data curation scripts that check if the data fall into the expected range and do not contain any discrepancies.

Figure 2 describes the clinical data collection process. The green parts are these steps of the process that are necessary, whereas the red parts are steps that are unnecessary in modern translational research. The green parts include the construction of the codebook, the eCRF creation, the data entry into the eCRF, the data integration into the database and the data analysis leading to results. The red parts include the creation of and the data entry into the paper CRF, which ideally should be avoided because this gives the data entry person double work. The information should be entered directly into the eCRF instead. The yellow line should only be followed when, even after carefully constructing the codebook, more data fields need to be included. Ideally, the codebook should also be compliant with ontologies for translational research such as the Basic Formal Ontology (BFO), the ontologies listed by the Open Biological and Biomedical Ontology (OBO) foundry and the Relation Ontology (RO) [46].

Fig. 2.

The clinical data collection process.

Other data types that are important in translational research, as listed in Fig. 1 (WP 2–5), often do not need a codebook because they are stored in standardized file formats such as Digital Imaging and Communications in Medicine (DICOM, for imaging and digital pathology data). However, information derived from these data types, such as PI-RADS scores (from prostate cancer MR images) and Gleason scores (from prostate cancer digital pathology images) should be stored in the codebook as well, to ensure that these values can be compared with the clinical data gathered in the eCRF.

2.4.Commandment 4: Make clear arrangements about data access

In large consortia, especially consortia with both academic partners and commercial partners, data access can be a sensitive issue. Therefore, it needs a clear arrangement up front. Of course, data access needs to be arranged in the informed consent as well, as patients are the data owners, and the General Data Protection Regulation (GDPR [10], within the EU) and the Health Insurance Portability and Accountability Act (HIPAA [12], within the US) have strict regulations about the patient’s privacy. The GDPR puts some constraints on data sharing, e.g., if a data controller wants to share data with a third party, and that third party is a processor, then a Data Processor Agreement (DPA) needs to be created. Furthermore, the informed consent that the patient signs before participating in a study needs to state clearly for what purposes their data will be used.

Ideally, at the end of the project, when all goals have been met and results have been published, the de-identified data should be shared with the whole world, if privacy regulations allow it. After all, the goal of a translational research project is to “translate” findings in fundamental research into medical practice and meaningful health outcomes, which can only be achieved if data are being shared as soon as possible, because then the whole world can use the data. This future public availability of the data should be included in the informed consent as well. As GDPR article 4 [10] states that “consent of the data subject means any freely given, specific, informed and unambiguous indication of the data subject’s wishes by which he or she, by a statement or by a clear affirmative action, signifies agreement to the processing of personal data relating to him or her”, this means that there should be a “yes/no” question or checkbox for the option to share the data in a public repository at the end of the study. If the patient answers “no” or does not check the box, the patient should still be allowed to enter the study, but his/her data cannot be submitted to a public repository.

2.5.Commandment 5: Agree about de-identification and anonymization

The responsibility for proper de-identification of the data often lies with the organization that collects the data (usually the hospital), because they are the ones that have the EHR. They should create a study subject ID for each subject, which can only be mapped to the original subject ID by a mapping table residing at the hospital. If the party performing the data integration receives data that are not properly de-identified by the hospital, the data should be destroyed immediately, the breach should be reported, and a new data submission should be requested. If a subject at any time requests to have his/her data removed from the central database, the hospital should inform the data manager about which data belonging to which study subject ID need to be removed. In the case that the data integration expert also needs to do the de-identification and anonymization, this should be arranged very clearly in the informed consent and the data processor agreement. For textual and numerical data, open-source software packages are available that can help with anonymization, such as the ARX anonymization tool [38]. For imaging data, anonymization tools are available that can strip any identifiable information from DICOM tags, such as the DICOM anonymizer [8] and the DicomCleaner [9].

2.6.Commandment 6: Reuse existing software where possible

There is usually no need to develop tools for data capturing, data management, data quality control, etc. from scratch, because there are many open source tools available for this, which can not only be used freely but also developed further. Within the Translational Research IT (TraIT) project [32] of the Center for Translational Molecular Medicine (CTMM), a list of suitable open source tools was created, which included OpenClinica [35], XNAT [29] and tranSMART [43]. For areas where there was no tool available, software was created. An overview of the TraIT tools can be found at https://trait.health-ri.nl/trait-tools/. Most translational research projects have similar problems, so when starting a new project, it is generally a good idea to see how they solved these problems, and if their solution can be reused. This reusability also increases the reproducibility of the research, because there is no reliance on obscure, custom-made computer scripts or websites. Table 1 shows an up-to-date list of freely available software related to translational research, including a description and the main data type it processes. This list was created by combining the overview of TraIT tools with an extensive PubMed search. It might be out of date soon, but provides a useful summary of the currently available tools.

2.7.Commandment 7: Make newly created software reusable

Although it is proposed at commandment 6 that existing software should be reused as much as possible, there might be cases where study-specific software needs to be created, for example to perform novel analyses. If there are no intellectual property issues, this newly created software can be submitted to repositories such as GitHub [37], SourceForge [47] or FigShare [44], or it can be made available on a custom-made website, and then referenced on Zenodo [52]. Griffin et al. [13] gives a good overview of the possibilities. This way, future translational researchers can reuse the software and do not need to reinvent the wheel. Github already hosts several scientific data management packages, such as Rucio (https://github.com/rucio/rucio), ISA tools (https://github.com/ISA-tools/isa-api) and Clowder (https://github.com/ncsa/clowder). There is also an overview of all 1,720 bioinformatics repositories on GitHub available [42]. If the software is submitted to one of these popular repositories, and it is accompanied with metadata that describes accurately what the software can do, it will be much easier for researchers to find the software and share it with other potential users.

2.8.Commandment 8: Adhere to the FAIR guiding principles

In 2016, the FAIR Guiding Principles for scientific data management and stewardship [50] were published. FAIR stands for the four foundational principles – Findability, Accessibility, Interoperability, and Reusability – that serve to guide data producers and publishers as they navigate around the obstacles around data management and stewardship. The difference with similar initiatives is that the FAIR principle do not only support the reuse of data by individuals, but also put emphasis on enhancing the ability of machines to automatically find and use the data. The elements of the FAIR Guiding Principles are related, but independent and separable:

The FAIR Guiding Principles should be applied to both data and software created in a translational research project, to achieve transparency and scientific reproducibility. An example of a FAIR-compliant dataset, is the Rembrandt brain cancer dataset [14]. This dataset is ‘findable’: it is hosted in the Georgetown Database of Cancer (G-DOC), with provenance and raw data available in the National Institute of Health (NIH) Gene Expression Omnibus (GEO) data repository. These resources are publicly available and thus ‘accessible’. The gene expression and copy number data are in standard data matrix formats that support formal sharing and satisfy the ‘interoperable’ condition. Finally, this dataset can be ‘reused’ for additional research through either the G-DOC platform or GEO.

2.9.Commandment 9: Make sure that successors are being instructed correctly

Translational research projects usually take 4 to 5 years. Clinicians, researchers and data managers, but also trial nurses, might come and go during this period. If these trial nurses performed the data entry for the study, they probably spent quite some time learning how to enter data into the eCRF. To avoid that the new data entry person needs to spend a similar amount of time to learn about this data entry, the old data entry person should properly instruct the new person, reducing the learning time. The same holds for the data managers. The leader of the data management work package (see commandment 1) might even make a data entry manual together with the data entry person, to ensure that any transfers of data entry tasks will go smoothly. As stated in commandment 2: data quality is extremely important and thus correct data entry should be a priority.

2.10.Commandment 10: Make it sustainable: What happens after the project?

When starting a new translational research project, big plans are made for the duration of the project, but very often not so much for the period after. What will happen when the project is finished? For example: who will pay for the continued storage of left-over biomaterials? Who will keep the database running? The researchers might even want to continue the project with yearly updates, because long-term follow-up information could be very valuable in these type of projects. Or they want to submit the data to a repository such as Dataverse [31] or Dryad [49], if the informed consent allows it. Publicly available datasets can be a goldmine for future research [18], certainly with the rise of artificial intelligence methods. At the start of the project, the researchers should already make a plan for what happens at the end of the study, when funding runs out, to avoid that data and biomaterials are lost for future research. This planning should also include a financial paragraph, because hosting of data (and storage of biomaterials) will need to be financed somehow, especially if the data are not submitted to a public repository.