Graphical Presentation of Patient-Treatment Interaction Elucidated by Continuous Biomarkers^[*]

 

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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.

^* Supplementary material published on our website https://doi.org/10.3414/ME16-01-0019

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• Supplementary Material