Statistical Modelling 23 (3) (2023), 228–246
A joint transition model for evaluating eGFR as biomarker for rejection after kidney transplantation
Maarten Coemans,
L-Biostat,
KU Leuven, Leuven,
Belgium.
Department of Microbiology,
Immunology and Transplantation,
KU Leuven, Leuven,
Belgium.
e-mail: maarten.coemans@kuleuven.be
Geert Verbeke,
I-Biostat,
Universiteit Hasselt & KU Leuven,
Hasselt & Leuven,
Belgium.
Maarten Naesens,
Department of Microbiology,
Immunology and Transplantation,
KU Leuven, Leuven,
Belgium.
Department of Nephrology and Renal Transplantation,
University Hospitals Leuven,
Leuven,
Belgium.
Abstract:
The estimated glomerular filtration rate (eGFR) quantifies kidney graft function and is measured repeatedly after transplantation. Kidney graft rejection is diagnosed by performing biopsies on a regular basis (protocol biopsies at time of stable eGFR) or by performing biopsies due to clinical cause (indication biopsies at time of declining eGFR). The diagnostic value of the eGFR evolution as biomarker for rejection is not well established. To this end, we built a joint model which combines characteristics of transition models and shared parameter models to carry over information from one biopsy to the next, taking into account the longitudinal information of eGFR collected in between. From our model, applied to data of University Hospitals Leuven (870 transplantations, 2635 biopsies), we conclude that a negative deviation from the mean eGFR slope increases the probability of rejection in indication biopsies, but that, on top of the biopsy history, there is little benefit in using the eGFR profile for diagnosing rejection. Methodologically, our model fills a gap in the biomarker literature by relating a frequently (repeatedly) measured continuous outcome with a less frequently (repeatedly) measured binary indicator. The developed joint transition model is flexible and applicable to multiple other research settings.
Keywords:
biomarker research, Kidney rejection, joint modelling, transition model, shared parameters
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Supplementary material.
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