Outcome Prediction for Unipolar Depression
Rubin, Mark A.
Cohen, Michael A.
Luciano, Joanne S.
Samson, Jacquelin A.
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Although effective drug and non-drug treatment for unipolar depressive illness exist, different individuals respond differently to different treatments. It is not uncommon for a given patient to lw switched several times from one treatment to another until an effective remedy for that particular patient is found. This process is costly in terms of time, money and suffering. It is thus desirable to determine at the outset the likdy response of a patient to the available treatments, so that the optimal one can be selected. Although prior attempts at outcome prediction with linear regression models have failed, recent work on this problem has indicated that the nonlinear predictive techniques of backpropagation and quadratic regression call account for a significant proportion of the variance in the data. The present research applies the nonlinear predictive technique of kernel regression to this problcrn, and employs cross-validation to test the ability of the resulting model to extract, from extremely noisy dinical data, information with predictive value. The importance of comparison with a suitable null hypothesis is illustrated.
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