Longitudinal Causal Effects of Normalized Protein Catabolic Rate on All-Cause Mortality in Patients With End-Stage Renal Disease: Adjusting for Time-Varying Confounders Using the G-Estimation Method

Am J Epidemiol. 2021 Jun 1;190(6):1133-1141. doi: 10.1093/aje/kwaa281.


In this study, we aimed to estimate the causal effect of normalized protein catabolic rate (nPCR) on mortality among end-stage renal disease (ESRD) patients in the presence of time-varying confounding affected by prior exposure using g-estimation. Information about 553 ESRD patients was retrospectively collected over an 8-year period (2011-2019) from hemodialysis facilities in Kerman, Iran. nPCR was dichotomized as <1.2 g/kg/day versus ≥1.2 g/kg/day. Then a standard time-varying accelerated failure time (AFT) Weibull model was built, and results were compared with those generated by g-estimation. After appropriate adjustment for time-varying confounders, weighted g-estimation yielded 78% shorter survival time (95% confidence interval (95% CI): -81, -73) among patients with a continuous nPCR <1.2 g/kg/day than among those who had nPCR ≥1.2 g/kg/day during follow-up, though it was 18% (95% CI: -57, 54) in the Weibull model. Moreover, hazard ratio estimates of 4.56 (95% CI: 3.69, 5.37) and 1.20 (95% CI: 0.66, 2.17) were obtained via weighted g-estimation and the Weibull model, respectively. G-estimation indicated that inadequate dietary protein intake characterized by nPCR increases all-cause mortality among ESRD patients, but the Weibull model provided an effect estimate that was substantially biased toward the null.

Keywords: G-estimation; epidemiologic methods; hemodialysis; mortality; time-varying confounding.

MeSH terms

  • Aged
  • Biomarkers / blood
  • Cause of Death
  • Dietary Proteins / metabolism
  • Female
  • Humans
  • Iran
  • Kidney Failure, Chronic / metabolism
  • Kidney Failure, Chronic / mortality*
  • Kidney Function Tests / statistics & numerical data*
  • Longitudinal Studies
  • Male
  • Middle Aged
  • Nutritional Status
  • Proportional Hazards Models
  • Renal Dialysis / mortality*
  • Retrospective Studies
  • Statistics as Topic
  • Time Factors*


  • Biomarkers
  • Dietary Proteins