Abstract
Modeling chronic kidney disease (CKD) in type 2 diabetes (T2D) is complex owing to the interplay of numerous risk factors, biomarkers, outcomes, and competing risks. One approach to capturing this interplay is the use of flexible frameworks such patient-level simulation (PLS) based on multivariable regression. The flexibility afforded by regression-based PLS allows a diverse array of risk factors to be captured and modeled. The present study aimed to review the markers, outcomes, risk factors, and sequelae captured in PLS models of CKD in T2D.
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