Summary
Background: Biomarkers providing evidence for patient-treatment interaction are key in the development
and practice of personalized medicine. Knowledge that a patient with a specific feature
– as demonstrated through a biomarker – would have an advantage under a given treatment
vs. a competing treatment can aid immensely in medical decision-making. Statistical
strategies to establish evidence of continuous biomarkers are complex and their formal
results are thus not easy to communicate. Good graphical representations would help
to translate such findings for use in the clinical community. Although general guidelines
on how to present figures in clinical reports are available, there remains little
guidance for figures elucidating the role of continuous biomarkers in patient-treatment
interaction (CBPTI).
Objectives: To combat the current lack of comprehensive reviews or adequate guides on graphical
presentation within this topic, our study proposes presentation principles for CBPTI
plots. In order to understand current practice, we review the development of CBPTI
methodology and how CBPTI plots are currently used in clinical research.
Methods: The quality of a CBPTI plot is determined by how well the presentation provides key
information for clinical decision-making. Several criteria for a good CBPTI plot are
proposed, including general principles of visual display, use of units presenting
absolute outcome measures, appropriate quantification of statistical uncertainty,
correct display of benchmarks, and informative content for answering clinical questions
especially on the quantitative advantage for an individual patient with regard to
a specific treatment. We examined the development of CBPTI methodology from the years
2000-2014, and reviewed how CBPTI plots were currently used in clinical research in
six major clinical journals from 2013-2014 using the principle of theoretical saturation. Each CBPTI plot found was assessed for appropriateness of its presentation and clinical
utility.
Results: In our review, a total of seven methodological papers and five clinical reports used
CBPTI plots which we categorized into four types: those that distinguish the outcome
effect for each treatment group; those that show the outcome differences between treatment
groups (by either partitioning all individuals into subpopulations or modelling the
functional form of the interaction); those that evaluate the proportion of population
impact of the biomarker; and those that show the classification accuracy of the biomarker.
The current practice of utilizing CBPTI plots in clinical reports suffers from methodological
shortcomings: the lack of presentation of statistical uncertainty, the outcome measure
scaled by relative unit instead of absolute unit, incorrect use of benchmarks, and
being non-informative in answering clinical questions.
Conclusions: There is considerable scope for improvement in the graphical representation of CBPTI
in clinical reports. The current challenge is to develop instruments for high-quality
graphical plots which not only convey quantitative concepts to readers with limited
statistical knowledge, but also facilitate medical decision-making.
Keywords
Graphical presentation - patient-treatment interaction - continuous biomarker - randomized
parallel-group controlled trial
