Application of multivariable optimal discriminant analysis in general internal medicine.
OBJECTIVE
To illustrate the use of multivariable optimal discriminant analysis (MultiODA).


METHODS
Data from four previously published studies were reanalyzed using MultiODA. The original analysis was Fisher's linear discriminant analysis (FLDA) for two studies and logistic regression analysis (LRA) for two studies.


RESULTS
In Study 1, FLDA achieved an overall percentage accuracy in classification (PAC) for the training sample of 69.9%, compared with 73.5% for MultiODA. In Study 2, the LRA model required three attributes to achieve a 76.1% overall PAC for the training sample and a 79.4% overall PAC for the hold-out sample. Using only two attributes, the MultiODA model achieved similar values. In Study 3, the FLDA model achieved an overall PAC of 82.5%, compared with 87.5% for the MultiODA model. In Study 4, MultiODA identified a two-attribute model that achieved a 93.3% overall training PAC, when an LRA model could not be developed.


CONCLUSIONS
MultiODA identified: a superior training model (Study 1); a more parsimonious model that achieved superior overall training and identical hold-out PAC (Study 2); a model that achieved a higher hold-out PAC (Study 3); and a two-attribute model that achieved a relatively high PAC when a multivariable LRA model could not be obtained (Study 4). These findings suggest that MultiODA has the potential to improve the accuracy of predictions made in general internal medicine research.
Yarnold
P R
PR
Department of Medicine, Northwestern University Medical School, Chicago, Illinois 60611, USA.
Soltysik
R C
RC
McCormick
W C
WC
Burns
R
R
Lin
E H
EH
Bush
T
T
Martin
G J
GJ
D000163:Acquired Immunodeficiency Syndrome / Q000628:therapy; D016002:Discriminant Analysis; D006801:Humans; D007388:Internal Medicine / Q000706:statistics & numerical data; D016015:Logistic Models; D008134:Long-Term Care / Q000706:statistics & numerical data*; D015999:Multivariate Analysis; D010351:Patient Discharge / Q000706:statistics & numerical data*; D010359:Patient Readmission / Q000706:statistics & numerical data*; D017060:Patient Satisfaction / Q000706:statistics & numerical data*; D012106:Research / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity