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Measurement of Lifespan in Drosophila melanogaster
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Time-varying effect modeling with longitudinal data truncated by death: conditional models, interpretations, and

Jason P Estes1, Danh V Nguyen2,3, Lorien S Dalrymple4

  • 1Department of Biostatistics, University of California, Los Angeles, 90095, California, U.S.A.

Statistics in Medicine
|December 10, 2015
PubMed
Summary
This summary is machine-generated.

Infection-related hospitalizations increase cardiovascular risks for dialysis patients. New models track these risks over time, accounting for high mortality in this population.

Keywords:
United States Renal Data Systemcardiovascular outcomesend-stage renal diseasefully conditional modelpartially linear generalized varying coefficient modelstime-varying effects

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Nephrology

Background:

  • Infection-related hospitalizations are linked to increased cardiovascular (CV) event risk in dialysis patients.
  • Understanding CV risk trajectories post-hospitalization is crucial for this high-mortality population.

Purpose of the Study:

  • To develop and compare novel statistical modeling tools for analyzing time-varying CV risk.
  • To examine CV outcome risk trajectories before and after infection-related hospitalizations in dialysis patients.

Main Methods:

  • Proposed partly conditional and fully conditional partially linear generalized varying coefficient models (PL-GVCMs).
  • Addressed longitudinal data with follow-up truncation by death, relevant for high-mortality populations.
  • Utilized hospitalization data from the United States Renal Data System and developed generalized likelihood ratio tests for inference.

Main Results:

  • Partly conditional models track survivor cohort risk trajectories; fully conditional models stratify by time of death.
  • Comparison of these models provides insights into CV risk dynamics in the dialysis population.
  • Simulation studies validated the estimation and inference procedures.

Conclusions:

  • The developed PL-GVCMs offer robust methods for analyzing time-varying effects in high-mortality populations.
  • These models are essential for accurately assessing CV risk trajectories in dialysis patients post-infection.
  • The findings aid in better understanding and managing cardiovascular complications in chronic kidney disease patients.