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

Background: 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). Study Design: A longitudin...

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Publicado en:Journal of Research in Health Sciences Vol. 23; no. 2; pp. 1 - 8
Autores principales: Gholian, Khadije, Hajian-Tilaki, Karimollah, Akbari, Roghayeh
Formato: research tables/charts Journal Article
Publicado: Hamadan University of Medical Sciences, School of Public Health Spring2023
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Spring2023
      vid: 23
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      pub: Hamadan University of Medical Sciences, School of Public Health
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        10.34172/jrhs.2023.117
        169841508
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        atl: Modeling Factors Associated with Dialysis Adequacy Using Longitudinal Data Analysis: Generalized Estimating Equation Versus Quadratic Inference Function.
      aug:
        au:
          Gholian, Khadije
          Hajian-Tilaki, Karimollah
          Akbari, Roghayeh
        affil: Student Research Center, Research Institute, Babol University of Medical Sciences, Babol, Iran
      sug:
        subj:
          Dialysis Psychosocial Factors
          Quality of Health Care Evaluation
          Models, Theoretical
          Human
          Prospective Studies
          Structural Equation Modeling
          Comparative Studies
          Dialysis Patients Psychosocial Factors
          Record Review
          Probability
          Male
          Female
          Sex Factors
          Age Factors
          Correlational Studies
          Hypertension
          Diabetes Mellitus
          Treatment Duration
          Male
          Female
      ab: Background: 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). Study Design: A longitudinal study. Methods: 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. Results: 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. Conclusion: 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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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