Strategies evaluation in environmental conditions by symbolic data analysis: application in medicine and epidemiology to trachoma

详细信息    查看全文

• 作者：Christiane Guinot (1)
  Denis Malvy (2)
  Jean-Fran莽ois Sch茅mann (3)
  Filipe Afonso (4)
  Raja Haddad (5)
  Edwin Diday (6)
  
  1. Laboratoire d鈥橧nformatique ; Universit茅 Fran莽ois Rabelais de Tours ; 37200 ; Tours ; France
  2. D茅partement de M茅decine Interne ; CHU St-Andr茅 and Universit茅 Victor Segalen Bordeaux 2 ; 33076 ; Bordeaux ; France
  3. Institut de Recherche pour le D茅veloppement ; Dakar ; Senegal
  4. SYROKKO ; A茅rop么le ; BP 13918 ; 95731 ; Roissy CDG ; France
  5. SYROKKO and LAMSADE ; Paris Dauphine University ; Place du Mar茅chal De Lattre De Tassigny ; 75016 ; Paris ; France
  6. CEREMADE ; Paris Dauphine University ; Paris ; France
  
• 关键词：Symbolic Data Analysis ; Multiple logistic regression ; Trachoma ; 62 ; 07 ; 62 ; 09
• 刊名：Advances in Data Analysis and Classification
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：9
• 期：1
• 页码：107-119
• 全文大小：1,000 KB
• 参考文献：1. Afonso F, Haddad R, Toque C, Eliezer ES, Diday E (2014) User manual of the SYR Software. Syrokko internal publication, p 70
  2. Billard, L, Diday, E (2003) From the statistics of data to the statistics of knowledge: symbolic data analysis. J Am Stat Assoc 98: pp. 470-487 CrossRef
  3. Billard, L, Diday, E (2006) Symbolic data analysis: conceptual statistics and data mining. Wiley, Chichester CrossRef
  4. Bock H, Diday E (2000) Analysis of symbolic data. In: Bock D (ed) Exploratory methods for extracting statistical information from complex data. Springer, Heidelberg, p 425. ISBN 3-540-66619-2
  5. Diday E (2011) Principal component analysis for categorical histogram data: some open directions of research. In: Fichet B, Piccolo D, Verde R, Vichi M (eds) Classification and multivariate analysis for complex data structures. Studies in classification, data analysis, and knowledge organization. Springer, Heidelberg, pp 3鈥?5
  6. Diday, E (2013) Principal component analysis for bar charts and metabins tables. Stat Anal Data Min 6: pp. 403-430
  7. Diday E, Afonso F, Haddad R (2013) The symbolic data analysis paradigm, discriminate discretization and financial application. In: Advances in Theory and Applications of High Dimensional and Symbolic Data Analysis, HDSDA 2013. Revue des Nouvelles Technologies de l鈥橧nformation, vol E-25. pp 1鈥?4
  8. Diday, E, Noirhomme, M (2008) Symbolic data analysis and the SODAS software. Wiley, Chichester
  9. Hosmer D, Lemeshow S (2000) Applied logistic regression. Wiley, New York. ISBN 0-471-61553-6
  10. Lee, J (1986) Insight on the use of multiple logistic regression analysis to estimate association between risk factor and disease occurrence. Int J Epidemiol 15: pp. 22-29 CrossRef
  11. Schemann, JF, Guinot, C, Traore, L, Sacko, D, Zefack, G, Dembele, M, Diallo, I, Malvy, D (2007) Longitudinal evaluation of three azithromycin distribution strategies for treatment of trachoma in a sub-saharan African country, Mali. Acta Trop 101: pp. 40-53 CrossRef
  12. Souza, RMCR, Queiroz, DCF, Cysneiros, FJA (2011) Logistic regression based pattern classifiers for symbolic interval data. Pattern Anal Appl 14: pp. 273-282 CrossRef
  13. WHO (1988) Programme for the prevention of blindness and deafness. In: Coding instructions for the WHO/PBL eye examination record (version iii). Tech. rep. World Health Organization, Gen猫ve
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Statistics
  Statistical Theory and Methods
  Statistics for Business, Economics, Mathematical Finance and Insurance
  Statistics for Life Sciences, Medicine and Health Sciences
  Statistics for Engineering, Physics, Computer Science, Chemistry and Geosciences
  Statistics for Social Science, Behavorial Science, Education, Public Policy and Law
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1862-5355

文摘

Trachoma, caused by repeated ocular infections with Chlamydia trachomatis whose vector is a fly, is an important cause of blindness in the world. We are presenting here an application of the Symbolic Data Analysis approach to an interventional study on trachoma conducted in Mali. This study was conducted to choose among three antibiotic strategies those with the best cost-effectiveness ratio and to find the demographic and environmental parameters on which we could try to intervene. The Symbolic Data Analysis approach aims at studying classes of individuals considered as new units. These units are described by variables whose values express for each class the variation of the values taken by each of its individuals. Finally, the results obtained are compared to those previously provided by multiple logistic regression analysis. Symbolic Data Analysis actually provides a new perspective on this study and suggests that some demographic, economics and environmental parameters are related to the disease and its evolution during the treatment, whatever the strategy. Moreover, it is shown that the efficiency of each strategy depends on environmental parameters.