TY - JOUR
T1 - Visualising statistical models using dynamic nomograms
AU - Jalali, Amirhossein
AU - Alvarez-Iglesias, Alberto
AU - Roshan, Davood
AU - Newell, John
N1 - Publisher Copyright:
© 2019 Jalali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - Translational Statistics proposes to promote the use of Statistics within research and improve the communication of statistical findings in an accurate and accessible manner to diverse audiences. When statistical models become more complex, it becomes harder to evaluate the role of explanatory variables on the response. For example, the interpretation and communication of the effect of predictors in regression models where interactions or smoothing splines are included can be challenging. Informative graphical representations of statistical models play a critical translational role; static nomograms are one such useful tool to visualise statistical models. In this paper, we propose the use of dynamic nomogram as a translational tool which can accommodate models of increased complexity. In theory, all models appearing in the literature could be accompanied by the corresponding dynamic nomogram to translate models in an informative manner. The R package presented will facilitate this communication for a variety of linear and non-linear models.
AB - Translational Statistics proposes to promote the use of Statistics within research and improve the communication of statistical findings in an accurate and accessible manner to diverse audiences. When statistical models become more complex, it becomes harder to evaluate the role of explanatory variables on the response. For example, the interpretation and communication of the effect of predictors in regression models where interactions or smoothing splines are included can be challenging. Informative graphical representations of statistical models play a critical translational role; static nomograms are one such useful tool to visualise statistical models. In this paper, we propose the use of dynamic nomogram as a translational tool which can accommodate models of increased complexity. In theory, all models appearing in the literature could be accompanied by the corresponding dynamic nomogram to translate models in an informative manner. The R package presented will facilitate this communication for a variety of linear and non-linear models.
UR - http://www.scopus.com/inward/record.url?scp=85075082950&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0225253
DO - 10.1371/journal.pone.0225253
M3 - Article
C2 - 31730633
AN - SCOPUS:85075082950
SN - 1932-6203
VL - 14
SP - e0225253
JO - PLoS ONE
JF - PLoS ONE
IS - 11
M1 - e0225253
ER -