Improved propensity matching for heart failure using neural gas and self-organizing maps

Leif E. Peterson, Sameer Ather, Vijay Divakaran, Anita Deswal, Biykem Bozkurt, Douglas L. Mann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We studied heart failure mortality and hospitalization of N=7,788 subjects in the Digitalis Intervention Group (DIG) clinical trial. Cases were defined as subjects with New York Heart Association (NYHA) class III-IV symptoms, while controls were defined as subjects with NYHA class I-II symptomatology. Controls were propensity matched with cases using logits from logistic regression, best winning nodes for neural gas and self-organizing maps, and k-means cluster analysis. Cox proportional hazards (PH) regression models were ran to determine the all-cause mortality and hospitalization hazard ratio (HR) for having NYHA functional class III-IV. Unmatched data resulted in a mortality HR of 1.28 (95% CI, 1.17-1.41), while logitbased propensity matching resulted in a mortality HR of 1.29 (95% CI, 1.15-1.44). When neural gas (NG) was used for propensity matching with normalized and standardized features, the mortality HR was 1.34 (95% CI, 1.19-1.50) and 1.05 (95% CI, 0.94-1.17), respectively. Propensity matching with self-organized maps (SOM) and normalized and standardized features yielded mortality HRs of 1.31 (95% CI, 1.16-1.46) and 1.05 (95% CI, 0.94-1.17), respectively. Crisp K-means cluster-based matching performed worse and biased the HRs towards the null value of HR=1. The strongest influence of matching was observed for NG when normalized features were used.

Original languageEnglish
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages2517-2524
Number of pages8
DOIs
StatePublished - Nov 20 2009
Externally publishedYes
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: Jun 14 2009Jun 19 2009

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2009 International Joint Conference on Neural Networks, IJCNN 2009
CountryUnited States
CityAtlanta, GA
Period06/14/0906/19/09

Fingerprint Dive into the research topics of 'Improved propensity matching for heart failure using neural gas and self-organizing maps'. Together they form a unique fingerprint.

Cite this