Two groups (Segal et al. Med Care. 2017;55(7):716-722; Segal et al. Am J Epidemiol. 2017;186(6):745-747; and Kim et al. J Gerontol A Biol Sci Med Sci. 2018;73(7):980-987) recently proposed methods for modeling frailty in studies where a reference standard frailty measure is not directly observed, but Medicare claims data are available. The groups use competing frailty measures, but the premise is similar: In a validation data set, model the frailty measure versus claims variables; in the primary data set, impute frailty status from claims variables, and conduct inference with those imputed values in place of the unobserved frailty measure. Potential use cases include risk prediction, confounding control, and prevalence estimation. In this commentary, we describe validity issues underlying these approaches, focusing mainly on risk prediction. Our main concern is that these approaches do not permit valid estimation of associations between the reference standard frailty measure (i.e., "frailty") and health outcomes. We argue that Segal's approach is akin to multiple imputation but with the outcome variable omitted from the imputation model, while Kim's is akin to regression calibration but with many variables improperly treated as surrogates. We discuss alternatives for risk prediction, including a secondary approach previously considered by Kim et al., and briefly comment on other use cases.
Keywords: multiple imputation; regression calibration; surrogacy; validation data.
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