IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v60y2005i11p2585-2595.html
   My bibliography  Save this article

Assessing patients' preferences for treatments for angina using a modified repertory grid method

Author

Listed:
  • Rowe, Gene
  • Lambert, Nigel
  • Bowling, Ann
  • Ebrahim, Shah
  • Wakeling, Ian
  • Thomson, Richard

Abstract

A current popular theme in medicine concerns whether and how patients should be involved in treatment choice. Assuming patient involvement is desirable, how should one go about eliciting preferences? A variety of quantitative and qualitative methods exist that may be used for this purpose, one of which is the repertory grid method. This method involves eliciting constructs (reasons) for preferences through comparing sets of three options. This method allows the structured elicitation of the reasons behind individual preferences, but also, when used with generalised procrustes analysis (GPA), allows aggregation of individual data to reveal general preference patterns. In this study the repertory grid method was used to examine patient preferences for angina treatments with the goal of, first, gaining some understanding of general patterns of patient preference, and second, examining the likely utility of the technique in this setting. A sample of 21 patients with mild and stable angina from two general practices in Norfolk, UK was interviewed using the repertory grid method to elicit the constructs underlying their preferences amongst seven angina treatments (including 'no treatment'). Individualised questionnaires were then produced and sent to the patients for self-completion, which required rating the extent to which each construct was relevant for each treatment (scored on visual analogue rating scales). Analysis of the ratings, using GPA, showed that the constructs clustered around two dimensions: 'some treatment' versus 'no treatment', and drug treatment versus surgical treatment. While some treatment was generally preferred to no treatment, individuals varied in preference for drug treatments or surgical treatments. Although the latter were generally perceived as 'effective' they were also perceived, for example, as 'invasive', 'frightening', related to 'negative experiences', and being more appropriate for when symptoms are severe ('proportionate'). We consider the implications of these results for involving patients in choosing amongst treatments.

Suggested Citation

  • Rowe, Gene & Lambert, Nigel & Bowling, Ann & Ebrahim, Shah & Wakeling, Ian & Thomson, Richard, 2005. "Assessing patients' preferences for treatments for angina using a modified repertory grid method," Social Science & Medicine, Elsevier, vol. 60(11), pages 2585-2595, June.
    • Handle: RePEc:eee:socmed:v:60:y:2005:i:11:p:2585-2595
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277-9536(04)00536-2
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. J. Gower, 1975. "Generalized procrustes analysis," Psychometrika, Springer;The Psychometric Society, vol. 40(1), pages 33-51, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Foster, Michele M. & Earl, Peter E. & Haines, Terry P. & Mitchell, Geoffrey K., 2010. "Unravelling the concept of consumer preference: Implications for health policy and optimal planning in primary care," Health Policy, Elsevier, vol. 97(2-3), pages 105-112, October.
    2. Frank Busing & Mark Rooij, 2009. "Unfolding Incomplete Data: Guidelines for Unfolding Row-Conditional Rank Order Data with Random Missings," Journal of Classification, Springer;The Classification Society, vol. 26(3), pages 329-360, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meyners, Michael & Qannari, El Mostafa, 2001. "Relating principal component analysis on merged data sets to a regression approach," Technical Reports 2001,47, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Juliana Martins Ruzante & Valerie J. Davidson & Julie Caswell & Aamir Fazil & John A. L. Cranfield & Spencer J. Henson & Sven M. Anders & Claudia Schmidt & Jeffrey M. Farber, 2010. "A Multifactorial Risk Prioritization Framework for Foodborne Pathogens," Risk Analysis, John Wiley & Sons, vol. 30(5), pages 724-742, May.
    3. Barbara McGillivray & Gard B. Jenset & Khalid Salama & Donna Schut, 2022. "Investigating patterns of change, stability, and interaction among scientific disciplines using embeddings," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-15, December.
    4. Wei Wang & Stephen J Lycett & Noreen von Cramon-Taubadel & Jennie J H Jin & Christopher J Bae, 2012. "Comparison of Handaxes from Bose Basin (China) and the Western Acheulean Indicates Convergence of Form, Not Cognitive Differences," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    5. Lisa Sakamoto & Hiromi Kajiya-Kanegae & Koji Noshita & Hideki Takanashi & Masaaki Kobayashi & Toru Kudo & Kentaro Yano & Tsuyoshi Tokunaga & Nobuhiro Tsutsumi & Hiroyoshi Iwata, 2019. "Comparison of shape quantification methods for genomic prediction, and genome-wide association study of sorghum seed morphology," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-15, November.
    6. Mardia, Kanti V. & Wiechers, Henrik & Eltzner, Benjamin & Huckemann, Stephan F., 2022. "Principal component analysis and clustering on manifolds," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    7. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    8. John Gower & Garmt Dijksterhuis, 1994. "Multivariate analysis of coffee images: A study in the simultaneous display of multivariate quantitative and qualitative variables for several assessors," Quality & Quantity: International Journal of Methodology, Springer, vol. 28(2), pages 165-184, May.
    9. Modroño Herrán, Juan Ignacio & Fernández Aguirre, María Carmen & Landaluce Calvo, M. Isabel, 2003. "Una propuesta para el análisis de tablas múltiples," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    10. Peter Verboon & Willem Heiser, 1992. "Resistant orthogonal procrustes analysis," Journal of Classification, Springer;The Classification Society, vol. 9(2), pages 237-256, December.
    11. Dahl, Tobias & Naes, Tormod, 2006. "A bridge between Tucker-1 and Carroll's generalized canonical analysis," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3086-3098, July.
    12. Young-Jin Kwon & Do-Hyun Kim & Byung-Chang Son & Kyoung-Ho Choi & Sungbok Kwak & Taehong Kim, 2022. "A Work-Related Musculoskeletal Disorders (WMSDs) Risk-Assessment System Using a Single-View Pose Estimation Model," IJERPH, MDPI, vol. 19(16), pages 1-19, August.
    13. V Alex Sotola & Cody A Craig & Peter J Pfaff & Jeremy D Maikoetter & Noland H Martin & Timothy H Bonner, 2019. "Effect of preservation on fish morphology over time: Implications for morphological studies," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.
    14. Thomas W. Davies & Philipp Gunz & Fred Spoor & Zeresenay Alemseged & Agness Gidna & Jean-Jacques Hublin & William H. Kimbel & Ottmar Kullmer & William P. Plummer & Clément Zanolli & Matthew M. Skinner, 2024. "Dental morphology in Homo habilis and its implications for the evolution of early Homo," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    15. Huckemann, Stephan & Hotz, Thomas, 2009. "Principal component geodesics for planar shape spaces," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 699-714, April.
    16. Erdem, Seda & Rigby, Dan, 2011. "Using Best Worst Scaling To Investigate Perceptions Of Control & Concern Over Food And Non-Food Risks," 85th Annual Conference, April 18-20, 2011, Warwick University, Coventry, UK 108790, Agricultural Economics Society.
    17. Ian L. Dryden & Jonathan D. Hirst & James L. Melville, 2007. "Statistical Analysis of Unlabeled Point Sets: Comparing Molecules in Chemoinformatics," Biometrics, The International Biometric Society, vol. 63(1), pages 237-251, March.
    18. Edmund Peay, 1988. "Multidimensional rotation and scaling of configurations to optimal agreement," Psychometrika, Springer;The Psychometric Society, vol. 53(2), pages 199-208, June.
    19. Bajocco, S. & Rosati, L. & Ricotta, C., 2010. "Knowing fire incidence through fuel phenology: A remotely sensed approach," Ecological Modelling, Elsevier, vol. 221(1), pages 59-66.
    20. Kensuke Okada & Shin-ichi Mayekawa, 2018. "Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling," Computational Statistics, Springer, vol. 33(3), pages 1457-1473, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:socmed:v:60:y:2005:i:11:p:2585-2595. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.