Strategies for handling missing data that improve Frailty Index estimation and predictive power: lessons from the NHANES dataset.

ABSTRACT: Missing data are ubiquitous in aging studies. Combining the National Health and Nutrition Examination Survey (NHANES) 2003/2004 and 2005/2006 cross-sectional aging studies (N = 9307), we investigated the effects of both real and simulated missing data on the Frailty Index (FI) and survival analysis, along with several mitigation strategies. We observed distinct block patterns of missing variables in the dataset. These blocks showed significant hazard rate (HR) differences when they were missing versus present, indicating that missingness cannot be simply ignored. Simulations of this patterned missingness produced a bias of 0.0112 ± 0.0008 to the mean FI when missing values were ignored, representing a change in hazard of 1.09 ± 0.01. A similar bias of 0.0106 ± 0.0001 was estimated in the r

SUBMITTER: Pridham G 

PROVIDER: S-EPMC9135945 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature