Diabetic kidney disease (DKD) is a common co-morbidity of diabetes, and much research is underway to identify early markers of DKD progression, i.e. estimated glomerular filtration rate (eGFR) change. A goal in DKD research is to develop prognostic models to accurately predict eGFR change. The linear mixed model with serial eGFR measures (outcomes) is the optimal statistical approach for prognostic model development, namely by evaluating the coefficient of the time*prognostic factor interaction term. However, two-stage methods that first estimate subject-specific eGFR slopes, and then use these outcomes in a regression framework, offer advantages as they are easy to interpret and can be readily implemented by researchers. In this work, we compared the linear mixed effects and two-stage methods, in terms of bias, efficiency and coverage via simulations and analytic methods, allowing for irregularly spaced measures, and varying degrees of missingness. We determined study design settings where the two-stage models performed competitively, thus offering a simple modeling alternative to DKD researchers. Notably, our work applies to other longitudinal marker-disease studies.