Analytic Considerations for Repeated Measures of eGFR in Cohort Studies of CKD

Abstract

Repeated measures of various biomarkers provide opportunities for us to enhance understanding of many important clinical aspects of CKD, including patterns of disease progression, rates of kidney function decline under different risk factors, and the degree of heterogeneity in disease manifestations across patients. However, because of unique features, such as correlations across visits and time dependency, these data must be appropriately handled using longitudinal data analysis methods. We provide a general overview of the characteristics of data collected in cohort studies and compare appropriate statistical methods for the analysis of longitudinal exposures and outcomes. We use examples from the Chronic Renal Insufficiency Cohort Study to illustrate these methods. More specifically, we model longitudinal kidney outcomes over annual clinical visits and assess the association with both baseline and longitudinal risk factors.

Keywords: Biomarkers; CKD; Chronic; Cohort Studies; Disease Progression; GEE; GFR; Humans; Renal Insufficiency; correlation structures; kidney; longitudinal data; mixed effects model; repeated measures; risk factors.

Figure 1.

Spaghetti plot of eGFR over time for 15 random Chronic Renal Insufficiency Cohort (CRIC) Study participants. The figure shows an example of the spaghetti plot of repeated eGFR values for 15 randomly selected CRIC Study participants, five from each of the three APOL1 risk categories. The spaghetti plot connects the longitudinal eGFR values within a single subject by lines over time but might result in overplotting with too many subjects on one plot.

Figure 2.

Heat map (left panel) and lasagna plot (right panel) plot on the basis of the same 15 subjects. Each row of the heat map represents eGFR values from one subject, and the color indicates the level of eGFR values. The lasagna plot is a heat map sorted by color gradients to reflect the overall eGFR distributions in the population at each visit.

Figure 3.

Schematic illustrations of linear mixed effects models with random intercepts (left panel) and both random intercepts and random slopes (right panel). The blue and red dots represent observed eGFR values for the two hypothetical subjects i and i′. The blue and red curves represent the mixed effects model fit on the basis of the data. The black lines represent the population trend estimated by generalized estimating equation (GEE). With the random intercept model, the two subjects have different baseline eGFR values but the same rate of decline. In the random intercept model, their rates of decline also differ. The deviations of intercepts and slopes are quantified using random effects parameters.