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Identifying Subjects Who Benefit from Additional Information for Better Prediction of the Outcome Variables
Author(s) -
Tian L.,
Cai T.,
Wei L. J.
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01125.x
Subject(s) - outcome (game theory) , inference , predictive value , value (mathematics) , computer science , population , medicine , machine learning , artificial intelligence , mathematics , environmental health , mathematical economics
Summary Suppose that we are interested in using new bio‐ or clinical markers, in addition to the conventional markers, to improve prediction or diagnosis of the patient's clinical outcome. The incremental value from the new markers is typically assessed by averaging across patients in the entire study population. However, when measuring the new markers is costly or invasive, an overall improvement does not justify measuring the new markers in all patients. A more practical strategy is to utilize the patient's conventional markers to decide whether the new markers are needed for improving prediction of his/her health outcomes. In this article, we propose inference procedures for the incremental values of new markers across various subgroups of patients classified by the conventional markers. The resulting point and interval estimates can be quite useful for medical decision makers seeking to balance the predictive or diagnostic value of new markers against their associated cost and risk. Our proposals are theoretically justified and illustrated empirically with two real examples.