Quantifying the Value of Biomarkers for Predicting Mortality

Noreen Goldman, Princeton University
Dana A. Glei, Georgetown University
Maxine Weinstein, Georgetown University

We apply three discrimination measures to evaluate the incremental value of biomarkers – above and beyond self-reported measures – for predicting all-cause mortality and to assess whether all three measures lead to the same conclusions. We use longitudinal data from a nationally representative sample of older Taiwanese (n = 639, aged 54+ in 2000, examined in 2000 and 2006, with mortality follow-up through 2011). The broad conclusions are consistent across discrimination measures: (1) inclusion of 19 biomarkers substantially enhances survival prediction compared with self-reports alone; (2) incorporating changes (2000-06) in biomarkers yields a moderate improvement over one-time measurement; and (3) inflammatory markers offer stronger prediction than either cardiovascular/metabolic or neuroendocrine measures. Although the rank ordering of individual biomarkers varies across discrimination measures, the following is true for the three measures: IL-6 is the strongest predictor, the other three inflammatory markers make the top 10, and homocysteine ranks second or third.

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 Presented in Session 221. Biodemography, Health, and Mortality