Citation

Ottenbacher KJ, Linn RT, Smith PM, Illig SB, Mancuso M, Granger CV. Ann. Epidemiol. 2004; 14(8): 551-559.

Affiliation

Division of Rehabilitation Sciences, University of Texas Medical Branch, Galveston, TX 77555-1137, USA. kottenba@utmb.edu

Copyright

(Copyright © 2004, American College of Epidemiology, Publisher Elsevier Publishing)

DOI

10.1016/j.annepidem.2003.10.005

PMID

15350954

Abstract

PURPOSE: Describe and compare the characteristics of artificial neural networks and logistic regression to develop prediction models in epidemiological research. METHODS: The sample included 3708 persons with hip fracture from 46 different states included in the Uniform Data System for Medical Rehabilitation. Mean age was 75.5 years (sd=14.2), 73.7% of patients were female, and 82% were non-Hispanic white. Average length of stay was 17.0 days (sd=10.6). The primary outcome measure was living setting (at home vs. not at home) at 80 to 180 days after discharge. RESULTS: Statistically significant variables (p <.05) in the logistic model included follow-up therapy, sphincter control, self-care ability, marital status, age, and length of stay. Areas under the receiver operating characteristic curves were 0.67 for logistic regression and 0.73 for neural network analysis. Calibration curves indicated a slightly better fit for the neural network model. CONCLUSIONS: Follow-up therapy and independent bowel and/or bladder function were strong predictors of living at home up to 6 months after hospitalization for hip fracture. No practical differences were found between the predictive ability of logistic regression and neural network analysis in this sample.

Language: en