A multivariate joint model to adjust for random measurement error while handling skewness and correlation in dietary data in an epidemiologic study of mortality.

PURPOSE: A substantial proportion of global deaths is attributed to unhealthy diet, which can be assessed at baseline or longitudinally. We demonstrated how to simultaneously correct for random measurement error, correlations, and skewness in the estimation of associations between dietary intake and all-cause mortality.

METHODS: We applied a multivariate joint model (MJM) that simultaneously corrected for random measurement error, skewness, and correlation among longitudinally measured intake levels of cholesterol, total fat, dietary fiber, and energy with all-cause mortality using US National Health and Nutrition Examination Survey linked to the National Death Index mortality data. We compared MJM with the mean method that assessed intake levels as the mean of a person's intake.

RESULTS: The estimates from MJM were larger than those from the mean method. For instance, the logarithm of hazard ratio (log HR) for dietary fiber intake increased by 14 times (from -0.04 to -0.60) with the MJM method. This translated into relative hazard of death of 0.55 (95% Credible Interval, CI: 0.45, 0.65) with the MJM and 0.96 (95% CI: 0.95, 0.97) with the mean method.

CONCLUSIONS: MJM adjusts for random measurement error and flexibly addresses correlations and skewness among longitudinal measures of dietary intake when estimating their associations with death.