Index or illusion: The case of frailty indices in the Health and Retirement Study

Introduction Frailty is a geriatric syndrome that has been defined differently with various indices. Without a uniform definition, it remains unclear how to interpret and compare different frailty indices (Fls). With the advances in index mining, we find it necessary to review the implicit assumptions about the creation of Fls. We are concerned the processing of frailty data may introduce measurement error and bias. We aim to review the assumptions, interpretability and predictive power of Fls regarding mortality. Methods Three Fls, the Functional Domains Model proposed by Strawbridge et al. (1998), the Burden Model by Rockwood et al. (2007) and the Biologic Syndrome Model by Fried et al. (2004), were directly compared using the data from the Health and Retirement Study (HRS), a longitudinal study since 1996 mainly following up Americans aged 50 years and over. The Fls were reproduced according to Cigolle et al. (2009) and interpreted with their input variables through forward-stepwise regression. Biases were the residuals of the Fls that could not be explained by own input variables. Any four of the input variables were used to create alternative indices. Discrete-time survival analysis was conducted to compare the predictive power of Fls, input variables and alternative indices on mortality. Results We found frailty a syndrome not unique to the elderly. The Fls were produced with different degrees of bias. The Fls could not be fully interpreted with the theory-based input variables. The bias induced by the Biological Syndrome Model better predicted mortality than frailty status. A complicated FI, the Burden Model, could be simplified. The input variables better predicted mortality than the Fls. The continuous Fls predicted mortality better than the frailty statuses. At least 6865 alternative indices better predicted mortality than the Fls. Conclusion Fls have been used as outcome in clinical trials and need to be reviewed for adequacy based on our findings. The three Fls are not closely linked to the theories because of bias introduced by data manipulation and excessive numbers of input variables. We are developing new algorithms to develop and validate innovative indices.