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A prediction method for multi-class systems based on limited data [clinical trials]

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2 Author(s)
Kuznetsov, V.A. ; Lab. Integrative & Med. Biophys., Nat. Inst. of Health, Bethesda, MD, USA ; Knott, G.D.

In many clinical trials, the prediction of patient outcome following therapy requires the analysis of two or more small groups of responders having a large number of simultaneously measured covariates, some of whose values may be absent. Prediction of individual outcomes in these groups is a severe statistical problem. This has motivated us to develop a suitable approach for inference from such limited data. A new statistically-oriented prediction method, called optimized independent segment voting (OISV), is presented for constructing a class-membership prediction function for such data sets. This “voting” prediction function is constructed based on the most informative and robust discrete segments of all covariate ranges, which are thus discretized

Published in:

Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on

Date of Conference:

2001

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