Background: Few studies have investigated long-term progression of kidney disease in patients with Type I diabetes. Methods: Using data from 1159 Type I diabetic patients with 10+years follow up, we used mixed models to compare linear and relative eGFR (log-eGFR) trajectory. Random intercept and slopes were included. We also tested if eGFR slopes varied by eGFR level at study entry, by including interaction terms of time and baseline eGFR level. Results: The sample was 48% male, with mean (SD) age 32 (9.8) years; 21 (8.5)years of diabetes, and 103.4 (29) ml/min/1.73m2 baseline eGFR. In the mixed models, eGFR slopes varied by initial eGFR level (likelihood ratio p < 0.001), and models adjusted for age and gender had better fit (p < 0.001). Correlations between observed and fitted values were 0.88-0.89 for the linear and loglinear models. Residual vs fitted and qq-plots indicated slightly better fit for the linear vs loglinear models. Conclusions: Linear and relative decline models gave similar predictions, but linear model had better fit. Inclusion of time*baseline eGFR interaction significantly improved fit, implying that rate of decline varies by starting eGFR-level.