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Review
Graphics and statistics for cardiology: clinical prediction rules
  1. Mark Woodward1,2,3,4,
  2. Hugh Tunstall-Pedoe3,
  3. Sanne AE Peters1
  1. 1The George Institute for Global Health, University of Oxford, Oxford, UK
  2. 2The George Institute for Global Health, University of Sydney, Sydney, Australia
  3. 3Cardiovascular Epidemiology Unit, Institute of Cardiovascular Research, University of Dundee, Dundee, UK
  4. 4Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
  1. Correspondence to Professor Mark Woodward, The George Institute for Global Health, Oxford Martin School, University of Oxford, 34 Broad Street, Oxford OX1 3BD, UK; mark.woodward{at}georgeinstitute.ox.ac.uk

Abstract

Graphs and tables are indispensable aids to quantitative research. When developing a clinical prediction rule that is based on a cardiovascular risk score, there are many visual displays that can assist in developing the underlying statistical model, testing the assumptions made in this model, evaluating and presenting the resultant score. All too often, researchers in this field follow formulaic recipes without exploring the issues of model selection and data presentation in a meaningful and thoughtful way. Some ideas on how to use visual displays to make wise decisions and present results that will both inform and attract the reader are given. Ideas are developed, and results tested, using subsets of the data that were used to develop the ASSIGN cardiovascular risk score, as used in Scotland.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

https://doi.org/10.1136/heartjnl-2016-310210

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    Footnotes

    • Contributors MW wrote the manuscript. HT-P and SAEP provided comments. SAEP wrote the R programs.

    • Competing interests MW is a consultant to Amgen.

    • Provenance and peer review Commissioned; internally peer reviewed.