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Classification and Regression Trees on Aggregate Data Modeling: An Application in Acute Myocardial Infarction

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Date
2011
Dewey
Probabilités et mathématiques appliquées
Sujet
Acute Myocardial Infarction; Aggregate Data Modeling; Regression Trees; Classification
Journal issue
Journal of Probability and Statistics
Volume
2011
Publication date
2011
Article pages
523937
Publisher
Hindawi
DOI
http://dx.doi.org/10.1155/2011/523937
URI
https://basepub.dauphine.fr/handle/123456789/8220
Collections
  • CEREMADE : Publications
Metadata
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Author
Quantin, Catherine
232969 Service Biostatistiques et Informatique Médicale (CHU de Dijon) [DIM]
Billard, Lynne
4979 Department of Statistics
Touati, Myriam
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Andreu, N.
232969 Service Biostatistiques et Informatique Médicale (CHU de Dijon) [DIM]
Cotin, Y.
Zeller, Manfred
Afonso, Filipe
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Battaglia, G.
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Seck, Djamal
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Le Teuff, G.
232969 Service Biostatistiques et Informatique Médicale (CHU de Dijon) [DIM]
Diday, Edwin
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Type
Article accepté pour publication ou publié
Abstract (EN)
Cardiologists are interested in determining whether the type of hospital pathway followed by a patient is predictive of survival. The study objective was to determine whether accounting for hospital pathways in the selection of prognostic factors of one-year survival after acute myocardial infarction AMI provided a more informative analysis than that obtained by the use of a standard regression tree analysis CART method . Information on AMI was collected for 1095 hospitalized patients over an 18-month period. The construction of pathways followed by patients produced symbolic-valued observations requiring a symbolic regression tree analysis. This analysis was compared with the standard CART analysis using patients as statistical units described by standard data selected TIMI score as the primary predictor variable. For the 1011 84, resp. patients with a lower higher TIMI score, the pathway variable did not appear as a diagnostic variable until the third second stage of the tree construction. For an ecological analysis, again TIMI score was the first predictor variable. However, in a symbolic regression tree analysis using hospital pathways as statistical units, the type of pathway followed was the key predictor variable, showing in particular that pathways involving early admission to cardiology units produced high one-year survival rates.

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