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Repeated longitudinal measurements are commonly used to model long-termdisease progression, and timing and number of assessments per patient may vary,leading to irregularly spaced and sparse data. Longitudinal trajectories mayexhibit curvilinear patterns, in which mixed linear regression methods may failto capture true trends in the data. We applied functional principal componentsanalysis to model kidney disease progression via estimated glomerularfiltration rate (eGFR) trajectories. In a cohort of 2641 participants withdiabetes and up to 15 years of annual follow-up from the Chronic RenalInsufficiency Cohort (CRIC) study, we detected novel dominant modes ofvariation and patterns of diabetic kidney disease (DKD) progression amongsubgroups defined by the presence of albuminuria. We conducted inferentialpermutation tests to assess differences in longitudinal eGFR patterns betweengroups. To determine whether fitting a full cohort model or separategroup-specific models is more optimal for modeling long-term trajectories, weevaluated model fit, using our goodness-of-fit procedure, and future predictionaccuracy. Our findings indicated advantages for both modeling approaches inaccomplishing different objectives. Beyond DKD, the methods described areapplicable to other settings with longitudinally assessed biomarkers asindicators of disease progression. Supplementary materials for this article areavailable online.

Inference and Prediction Using Functional Principal Components Analysis:  Application to Diabetic Kidney Disease Progression in the Chronic Renal  Insufficiency Cohort (CRIC) Study