  Identifying prognostic factors for disease progression is a cornerstone of
medical research. Repeated assessments of a marker outcome are often used to
evaluate disease progression, and the primary research question is to identify
factors associated with the longitudinal trajectory of this marker. Our work is
motivated by diabetic kidney disease (DKD), where serial measures of estimated
glomerular filtration rate (eGFR) are the longitudinal measure of kidney
function, and there is notable interest in identifying factors, such as
metabolites, that are prognostic for DKD progression. Linear mixed models (LMM)
with serial marker outcomes (e.g., eGFR) are a standard approach for prognostic
model development, namely by evaluating the time and prognostic factor (e.g.,
metabolite) interaction. However, two-stage methods that first estimate
individual-specific eGFR slopes, and then use these as outcomes in a regression
framework with metabolites as predictors are easy to interpret and implement
for applied researchers. Herein, we compared the LMM and two-stage methods, in
terms of bias and mean squared error via analytic methods and simulations,
allowing for irregularly spaced measures and missingness. Our findings provide
novel insights into when two-stage methods are suitable longitudinal prognostic
modeling alternatives to the LMM. Notably, our findings generalize to other
disease studies.
