Modeling Factors Associated with Dialysis Adequacy Using Longitudinal Data Analysis: Generalized Estimating Equation Versus Quadratic Inference Function.

Abstract

In hemodialysis patients, changes in dialysis adequacy (DA) are examined longitudinally. The aim of this study was to determine factors affecting DA using the generalized estimating equation (GEE) and to compare them with the quadratic inference function (QIF). A longitudinal study. This longitudinal study examined the records of 153 end-stage renal disease (ESRD) patients. The longitudinal data on the DA and baseline demographic and clinical characteristics were obtained from patients' files. The GEE1, GEE2, and QIF models were fitted with different correlation structures, and then the best correlation structure was selected using the quasi-likelihood information criterion (QIC), Akaike information criterion (AIC), and Bayes information criterion (BIC) fitting criteria. The majority of patients (59.5%) had unfavorable DA (KT/V<1.2). Women and patients<60 years had more favorable DA. In the GEE model, the coefficients of female gender (β=0.079, 95% confidence interval [CI]: 0.032, 0.062), age at starting dialysis (β=-0.002, 95% CI: -0.004, -0.0001), hypertension (HTN, β=-0.055, 95% CI: -0.007, -0.103), diabetes (β=-0.088,95% CI: -0.021, -0.155), dialysis duration (β=0.132, 95% CI: 0.085, 0.178), and weight (β=-0.004, 95% CI: -0.006, -0.003) demonstrated a significant relationship with DA. The three models resulted in a similar estimate of regression coefficients. The relative efficiencies of QIF versus GEE1, QIF versus GEE2, and GEE2 versus GEE1 were 1.175, 1.056, and 1.113, respectively. DA is not optimal in most hemodialysis patients, and gender, age at the start of dialysis, HTN, diabetes, dialysis duration, and weight had a significant association with DA. The three different models yielded quite similar coefficient estimates, but the QIF model resulted more efficient than GEE1 and GEE2.