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effi cacy of the specifi c diagnostic or therapeutic strategy. In many cases, pre-clinical
studies lead to new basic science questions that must be answered prior to moving
forward with the translation of the clinical end-point. This leads to an iterative
refi nement cycle. Once such iterative refi nement between basic science and pre-
clinical research comes to fruition, the resulting diagnostic or therapeutic strategy is
translated into early clinical studies per the processes and activities described earlier
in this chapter.
6.2.2.2
Pragmatic Research
In the follow-on phase to clinical research, pragmatic scientifi c questions are posed
and answered through the assessment of data generated either: (1) during the course
of standard-of-care activities; or (2) public-domain data sets resulting from histori-
cal clinical studies. These questions can include basic associations between the vari-
ous dimensions of diagnostic or treatment modalities and the outcomes experienced
by patients (including health status, quality of life, or cost). When such associations
are found to be present and of interest, they may be further explored to determine
what hypothesis can be formulated as to their biological or mechanistic bases, thus
generating research questions that can be “fed back” into the translational cycle and
used by basic scientists to inform new lines of laboratory based investigation. Of
note, in some instances, such pragmatic research, if yielding purely clinical hypoth-
esis, can lead to direct feedback to the clinical research process without the neces-
sity for basic science and pre-clinical research.
6.3
The Role of Informatics in Clinical and Translational
Research
The benefi ts made possible by using BMI theories and methods to address the infor-
mation needs associated with the CTR paradigm have been described frequently in
the literature [
1
,
4
,
7
,
13
]. In general, the use of BMI theories and methods in this
context can be aligned with one or more of the following problems area:
6.3.1
The Collection and Management of Heterogeneous
and Multi-dimensional Data Sets
With the increasing availability of high-throughput data sources, such as electronic
health records (EHRs) and clinical research management systems (CRMS) or
Electronic Data Capture (EDC) tools, as well as 'omics' instrumentation, the size
and complexity of data sets that researchers must collect, store and retrieve on a
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