A quantile frailty index without dichotomization

Summary measures of health quantify the aging process of individuals. They should be interpretable, associated with future adverse outcomes, and straightforward to assemble. We use the rank-ordering of risk within a population to construct a quantile frailty index (QFI) that avoids dichotomization, is convenient and interpretable, and is associated with adverse outcomes. We show that the QFI outperforms previous frailty index (FI) measures on cross-sectional laboratory data (NHANES, CSHA, and ELSA). We construct the QFI by ranking the risk of individuals with respect to a reference population. Sex-specific reference populations narrow male–female FI differences as a function of age, and improve predictive performance. With a fixed reference population of 80–85 year olds, our QFI appears similar to earlier FI measures. With an age-matched reference population for each individual, we obtain a QFI that contains very little age information and that has similar predictive performance as other age-controlled FI measures. Adding age as an auxiliary variable leads to significantly better performance. We conclude that age should be controlled for when evaluating the predictive performance of summary measures of health. This is straight-forward to do with the QFI.