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Correspondence Analysis

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Machine Learning in Medicine

Abstract

Multiple treatments for one condition are increasingly available, and a systematic assessment would serve optimal care. Research in this field to date is problematic.

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References

  1. Caldwell D, Ades A, Higgins J (2005) Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ 331:897–904

    Article  PubMed  Google Scholar 

  2. Sparapani R (2005) Multiple treatments, confounding and the propensity score. www.mcw.edu. 20 Nov 2012

  3. Cooper N, Peters J, Lai M, Juni P, Wandel S, Palmer S, Paulden M, Conti S, Welton N, Abrams K, Bujkiewicz S, Spiegelhalter D, Sutton A (2011) How valuable are multiple treatment comparison methods in evidence-based healthcare evaluation? Value Health 14:371–380

    Article  PubMed  Google Scholar 

  4. Joffe M, Rosenbaum P (1999) Propensity scores. Am J Epidemiol 4:327–331

    Article  Google Scholar 

  5. Benzecri JP (1973) L‘analyse de donnees, Vol 2. L‘analyse de correspondances. Edit by Dunod, Paris

    Google Scholar 

  6. SPSS statistical software. www.spss.com. 20 Nov 2012

  7. Hoffman DL, Franke GR (1986) Correspondence analysis: graphical representation of categorical data in marketing research. J Market Res 23:213–227

    Article  Google Scholar 

  8. Bendixen M (2003) A practical guide to the use of correspondence analysis in marketing research. Market Bull 14:1–15

    Google Scholar 

  9. Javalgi R, Whipple T, McManamon M, Edick V (1992) Hospital image: a correspondence analysis. J Health Care Market 12:34–41

    CAS  Google Scholar 

  10. Sourial N, Wolfson C, Zhu B, Quail J, Fletcher J, Karunananthan S, Bandeen-Roche K, Beland F, Bergman H (2010) Correspondence analysis is a useful tool to uncover the relationships among categorical variables. J Clin Epidemiol 63:638–646

    Article  PubMed  Google Scholar 

  11. Tan Q, Brusgaard K, Kruse TA, Oakeley E, Hemmings B, Beck-Nielsen H, Hansen L, Gaster M (2004) Correspondence analysis of microarray time-course data in case–control design. J Biomed Inform 37:358–365

    Article  PubMed  CAS  Google Scholar 

  12. Williams L (2010) A tutorial on multiblock discriminant correspondence analysis: a new method for analyzing discourse data from clinical populations. J Speech Lang Hear Res 53:1372–1393

    Article  PubMed  Google Scholar 

  13. Beaton D, Abdi H (2011) Partial least squares-correspondence analysis: a new method to analyze common patterns in measures of cognition and genetics. Neuroinformatics www.neuroinformatics2011.org. 20 Nov 2012

  14. Almeida R, Infantosi A, Suassuna J, Costa J (2009) Multiple correspondence analysis in predictive logistic modeling: application to living-donor kidney transplant data. J Comput Method Programs Biomed 95:116–128

    Article  Google Scholar 

  15. Crichton N, Hinde J (1989) Correspondence analysis as a screening method for indicants for clinical diagnosis. Stat Med 8:1351–1362

    Article  PubMed  CAS  Google Scholar 

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Appendix

Appendix

Data file of the example used in the chapter: 217 patients were randomly treated with one of three treatments (treat = treatment) and produced one of three responses (1 = complete remission, 2 = partial remission, 3 = no response)

Table 5

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Cleophas, T.J., Zwinderman, A.H. (2013). Correspondence Analysis. In: Machine Learning in Medicine. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6886-4_13

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