TY - JOUR
T1 - A note on proposed estimation procedures for claims-based frailty indexes
AU - Van Domelen, Dane R.
AU - Bandeen-Roche, Karen
N1 - Publisher Copyright:
© 2020 Oxford University Press. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Multiple imputation
KW - Regression calibration
KW - Surrogacy
KW - Validation data
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U2 - 10.1093/AJE/KWZ247
DO - 10.1093/AJE/KWZ247
M3 - Review article
C2 - 31673711
AN - SCOPUS:85086792723
SN - 0002-9262
VL - 189
SP - 369
EP - 371
JO - American journal of epidemiology
JF - American journal of epidemiology
IS - 5
ER -