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Functional joint models for chronic kidney disease in kidney transplant recipients

Jianghu James Dong1,2, Jiguo Cao3, Jagbir Gill4

  • 1Department of Biostatistics, University of Nebraska Medical Center, NE, USA.

Statistical Methods in Medical Research
|May 10, 2021
PubMed

Insights

This study introduces a new functional joint model to extend transplanted kidney life by analyzing chronic kidney disease progression and patient survival. The model aids in maximizing scarce organ resources for kidney transplant recipients.

Area of Science:

  • Nephrology
  • Biostatistics
  • Medical Statistics

Background:

  • Kidney transplantation is crucial for end-stage renal disease patients due to organ scarcity.
  • Extending transplanted kidney survival is vital for maximizing organ resource utilization.
  • Chronic kidney disease progression monitoring and patient survival outcomes are key clinical concerns.

Purpose of the Study:

  • To propose a novel functional joint model for analyzing longitudinal kidney function and competing risks survival data post-kidney transplantation.
  • To investigate methods for extending transplanted kidney life by modeling disease progression and patient survival simultaneously.
  • To address the gap in research on joint modeling for multiple clinical outcomes in kidney transplant recipients.

Main Methods:

  • Development of a functional joint model incorporating a longitudinal sub-model with flexible basis functions for subject-level trajectories.
  • Inclusion of a competing risks sub-model to handle multiple time-to-event outcomes (kidney transplant failure, death with/without function).
  • Utilizing time-dependent functions of shared random effects to link longitudinal and survival sub-models, extending standard linear joint models.

Main Results:

  • The proposed functional joint model effectively integrates longitudinal kidney function data (glomerular filtration rate) with competing risks survival data.
  • The model allows for flexible characterization of individual patient trajectories and their association with survival outcomes.
  • Demonstrated the utility of the joint model in providing clinical insights for post-kidney transplantation management.

Conclusions:

  • The novel functional joint model offers a powerful statistical framework for analyzing complex outcomes in kidney transplant recipients.
  • This approach can provide valuable clinical references for managing chronic kidney disease progression and improving patient survival.
  • The model contributes to the advancement of statistical methodologies for scarce resource allocation in organ transplantation.
Keywords:
CKDCompeting-risks survivalbasis functionjoint modelsrandom effects

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