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A systematic review of statistical methodology used to evaluate progression of chronic kidney disease using electronic healthcare records.

Faye ClearyDavid Prieto-MerinoDorothea Nitsch
Published in: PloS one (2022)
Studies based on renal function tests in EHRs may have overstated reliability of findings in the presence of informative missingness. Future renal research requires more explicit statements of data completeness and consideration of i) selection bias and representativeness of sample to the intended target population, ii) ascertainment bias where follow-up depends on risk, and iii) the impact of competing mortality. We recommend that renal progression studies should use statistical methods that take into account variability in renal function, informative censoring and population heterogeneity as appropriate to the study question.
Keyphrases
  • chronic kidney disease
  • healthcare
  • case control
  • end stage renal disease
  • electronic health record
  • big data
  • type diabetes
  • cardiovascular disease
  • deep learning
  • data analysis
  • health insurance