Volume 47, Issue 2
Original Paper

Tutorial: Using Confidence Curves in Medical Research

Ralf Bender

Corresponding Author

E-mail address: Ralf.Bender@iqwig.de

Institute for Quality and Efficiency in Health Care, Cologne, Germany

Phone: +49 221 35685‐451, Fax: +49 221 35685‐891Search for more papers by this author
Gabriele Berg

Department of Epidemiology and Medical Statistics, School of Public Health, University of Bielefeld, Germany

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Hajo Zeeb

Department of Epidemiology and Medical Statistics, School of Public Health, University of Bielefeld, Germany

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First published: 13 April 2005
Citations: 9

Abstract

Confidence intervals represent a routinely used standard method to document the uncertainty of estimated effects. In most cases, for the calculation of confidence intervals the conventional fixed 95% confidence level is used. Confidence curves represent a graphical illustration of confidence intervals for confidence levels varying between 0 and 100%. Although such graphs have been repeatedly proposed under different names during the last 40 years, confidence curves are rarely used in medical research. In this paper, we introduce confidence curves and present a short historical review. We draw attention to the different interpretation of one‐ and two‐sided statistical inference. It is shown that these two options also have influence on the plotting of appropriate confidence curves. We illustrate the use of one‐ and two‐sided confidence curves and explain their correct interpretation. In medical research more emphasis on the choice between the one‐ and two‐sided approaches should be given. One‐ and two‐sided confidence curves are useful complements to the conventional methods of presenting study results. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Number of times cited according to CrossRef: 9

  • Beyond the forest plot: The drapery plot, Research Synthesis Methods, 10.1002/jrsm.1410, 0, 0, (2020).
  • P value functions: An underused method to present research results and to promote quantitative reasoning, Statistics in Medicine, 10.1002/sim.8293, 38, 21, (4189-4197), (2019).
  • Fusion learning for inter-laboratory comparisons, Journal of Statistical Planning and Inference, 10.1016/j.jspi.2017.09.011, 195, (64-79), (2018).
  • Frequency-calibrated belief functions: Review and new insights, International Journal of Approximate Reasoning, 10.1016/j.ijar.2017.10.013, 92, (232-254), (2018).
  • Confidence distributions: A review, Statistical Methodology, 10.1016/j.stamet.2014.07.002, 22, (23-46), (2015).
  • Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review, International Statistical Review, 10.1111/insr.12000, 81, 1, (3-39), (2013).
  • Learning About Parameters, and Some Notes on Planning, Understanding Biostatistics, 10.1002/9781119992677, (119-148), (2011).
  • Final Collapse of the Neyman-Pearson Decision Theoretic Framework and Rise of the neoFisherian, Annales Zoologici Fennici, 10.5735/086.046.0501, 46, 5, (311-349), (2009).
  • Year 2005 – Report, Biometrical Journal, 10.1002/bimj.200410211, 48, 2, (189-192), (2006).

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