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Licensed Unlicensed Requires Authentication Published by De Gruyter October 1, 2016

How to analyze the Visual Analogue Scale: Myths, truths and clinical relevance

Gillian Z. Heller, Maurizio Manuguerra and Roberta Chow


Background and aims

The Visual Analogue Scale (VAS) is a popular tool for the measurement of pain. A variety of statistical methods are employed for its analysis as an outcome measure, not all of them optimal or appropriate. An issue which has attracted much discussion in the literature is whether VAS is at a ratio or ordinal level of measurement. This decision has an influence on the appropriate method of analysis. The aim of this article is to provide an overview of current practice in the analysis of VAS scores, to propose a method of analysis which avoids the shortcomings of more traditional approaches, and to provide best practice recommendations for the analysis of VAS scores.


We report on the current usage of statistical methods, which fall broadly into two categories: those that assume a probability distribution for VAS, and those that do not. We give an overview of these methods, and propose continuous ordinal regression, an extension of current ordinal regression methodology, which is appropriate for VAS at an ordinal level of measurement. We demonstrate the analysis of a published data set using a variety of methods, and use simulation to compare the power of the various methods to detect treatment differences, in differing pain situations.


We demonstrate that continuous ordinal regression provides the most powerful statistical analysis under a variety of conditions.

Conclusions and Implications

We recommend that in the situation in which no covariates besides treatment group are included in the analysis, distribution-free methods (Wilcoxon, Mann–Whitney) be used, as their power is indistinguishable from that of the proposed method. In the situation in which there are covariates which affect VAS, the proposed method is optimal. However, in this case, if the VAS scores are not concentrated around either extreme of the scale, normal-distribution methods (t-test, linear regression) are almost as powerful, and are recommended as a pragmatic choice. In the case of small sample size and VAS skewed to either extreme of the scale, the proposed method has vastly superior power to other methods.

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  1. Ethical issues: The article uses previously published data and a simulation study, and therefore was not subject to ethics approval.

  2. Funding and conflict of Interest: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.


The authors thank Dr Greg Glazov for his assistance.


[1] Freyd M. The graphic rating scale. J Educ Psychol 1923;14:83–102.10.1037/h0074329Search in Google Scholar

[2] Bond MR, Pilowsky I. Subjective assessment of pain and its relationship to the administration of analgesics in patients with advanced cancer. J Psychosom Res 1966;10:203–8.10.1016/0022-3999(66)90064-XSearch in Google Scholar

[3] Scott J, Huskisson EC. Graphic representation of pain. Pain 1976;2:175–84.10.1016/0304-3959(76)90113-5Search in Google Scholar

[4] Merskey H, Bogduk N. Classification of chronic pain, IASP Task Force on Taxonomy. Seattle, WA: International Association for the Study of Pain Press; 1994.Search in Google Scholar

[5] Breivik EK, Bjørnsson GA, Skovlund E. A comparison of pain rating scales by sampling from clinical data. Clin J Pain 2000;16:22–8.10.1097/00002508-200003000-00005Search in Google Scholar

[6] Williamson A, Hoggart B. Pain: a review of three commonly used pain rating scales. J Clin Nurs 2005;14:798–804.10.1111/j.1365-2702.2005.01121.xSearch in Google Scholar

[7] Hawker GA, Mian S, Kendzerska T, French M. Measures of adult pain: Visual Analog Scale for pain (VAS pain), Numeric Rating Scale for pain (NRS pain), Mcgill Pain Questionnaire (MPQ), Short-Form Mcgill Pain Questionnaire (SFMPQ), Chronic Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF-36 BPS), and measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care Res 2011;63:S240–52.10.1002/acr.20543Search in Google Scholar

[8] Jensen MP, Karoly P, Braver S. The measurement of clinical pain intensity: a comparison of six methods. Pain 1986;27:117–26.10.1016/0304-3959(86)90228-9Search in Google Scholar

[9] Dexter F, Chestnut DH. Analysis of statistical tests to compare visual analog scale measurements among groups. Anesthesiology 1995;82:896–902.10.1097/00000542-199504000-00012Search in Google Scholar PubMed

[10] Stevens SS. On the theory of scales of measurement. Science 1946;103:677–80.10.1126/science.103.2684.677Search in Google Scholar PubMed

[11] Philip BK. Parametric statistics for evaluation of the visual analog scale. Anesth Analg 1990;71:710.10.1213/00000539-199012000-00027Search in Google Scholar PubMed

[12] Wewers ME, Lowe NK. A critical review of visual analogue scales in the measurement of clinical phenomena. Res Nurs Health 1990;13:227–36.10.1002/nur.4770130405Search in Google Scholar

[13] Svensson E. Comparison of the quality of assessments using continuous and discrete ordinal rating scales. Biom J 2000;42:417–34.10.1002/1521-4036(200008)42:4<417::AID-BIMJ417>3.0.CO;2-ZSearch in Google Scholar

[14] Kersten P, Küç ükdeveci AA, Tennant A. The use of the Visual Analogue Scale (VAS) in rehabilitation outcomes. J Rehabil Med 2012;44:609–10.10.2340/16501977-0999Search in Google Scholar

[15] Kersten P, Küç ükdeveci AA, Tennant A. How should we use the Visual Analogue Scale (VAS) in rehabilitation outcomes? IV: Reply on How should we use the Visual Analogue Scale (VAS) in rehabilitation outcomes. J Rehabil Med 2012;44:803–4.10.2340/16501977-1044Search in Google Scholar

[16] Price DD, McGrath PA, Rafii A, Buckingham B. The validation of visual analogue scales as ratio scale measures for chronic and experimental pain. Pain 1983;17:45–56.10.1016/0304-3959(83)90126-4Search in Google Scholar

[17] Price DD, Staud R, Robinson ME. How should we use the visual analogue scale (VAS) in rehabilitation outcomes? II: visual analogue scales as ratio scales: an alternative to the view of Kersten. J Rehabil Med 2012;44:800–1.10.2340/16501977-1031Search in Google Scholar

[18] Forrest M, Andersen B. Ordinal scale and statistics in medical research. BMJ 1986;292:537–8.10.1136/bmj.292.6519.537Search in Google Scholar

[19] Lindsey JK. Applying generalized linear models. Springer Science & Business Media; 1997.Search in Google Scholar

[20] Fodstad K, Staff AC, Laine K. Effect of different episiotomy techniques on perineal pain and sexual activity 3 months after delivery. Int Urogynecol J 2014;25:1629–37.10.1007/s00192-014-2401-2Search in Google Scholar

[21] Ananth CV, Kleinbaum DG. Regression models for ordinal responses: a review of methods and applications. Int J Epidemiol 1997;26:1323–33.10.1093/ije/26.6.1323Search in Google Scholar

[22] Butler PV. Linear analogue self-assessment and procrustean measurement: a critical review of visual analogue scaling in pain assessment. J Clin Psychol Med Settings 1997;4:111–29.10.1023/A:1026240322240Search in Google Scholar

[23] Manuguerra M, Heller GZ. Ordinal regression models for continuous scales. Int J Biostat 2010;6.10.2202/1557-4679.1230Search in Google Scholar

[24] R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2015 in Google Scholar

[25] Manuguerra M, Heller GZ. ordinalCont: ordinal regression analysis for continuous scales. R package version 0.3; 2015 in Google Scholar

[26] Nakamura T, Ebihara S, Ohkuni I, Izukura H, Harada T, Ushigome N, Ohshiro T, Musha Y, Takajashi H, Tsuchiya K, Kubota A. Low level laser therapy for chronic knee joint pain patients. Laser Ther 2014;23:273–7.10.5978/islsm.14-OR-21Search in Google Scholar

[27] Dworkin RH, Turk DC, Farrar JT, Haythornthwaite JA, Jensen MP, Katz NP, Kerns RD, Stucki G, Allen RR, Bellamy N, Carr DB. Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. Pain 2005;113: 9–19.10.1016/j.pain.2004.09.012Search in Google Scholar

[28] Turk DC, Dworkin RH, Revicki D, Harding G, Burke LB, Cella D, Cleeland CS, Cowan P, Farrar JT, Hertz S, Max MB. Identifying important outcome domains for chronic pain clinical trials: an IMMPACT survey of people with pain. Pain 2008;137:276–85.10.1016/j.pain.2007.09.002Search in Google Scholar

[29] Park BU, Mammen E, Lee YK, Lee ER. Varying coefficient regression models: a review and new developments. Int Stat Rev 2015;83:36–64.10.1111/insr.12029Search in Google Scholar

[30] Wood S. Generalized additive models: an introduction with R. CRC Press; 2006.10.1201/9781420010404Search in Google Scholar

[31] Altman DG. Statistical reviewing for medical journals. Stat Med 1998;17:2661–74.10.1002/(SICI)1097-0258(19981215)17:23<2661::AID-SIM33>3.0.CO;2-BSearch in Google Scholar

[32] Reddy BS. The epidemic of unrelieved chronic pain: the ethical, societal, and regulatory barriers facing opioid prescribing physicians. J Leg Med 2006;27:427–42.10.1080/01947640601021048Search in Google Scholar

[33] Svensson E. Different ranking approaches defining association and agreement measures of paired ordinal data. Stat Med 2012;31:3104–17.10.1002/sim.5382Search in Google Scholar

Received: 2016-03-16
Revised: 2016-06-07
Accepted: 2016-06-30
Published Online: 2016-10-01
Published in Print: 2016-10-01

© 2016 Scandinavian Association for the Study of Pain