Joint Modeling of Repeated Measures and Competing Failure Events in a Study of Chronic Kidney Disease

Wei Yang (), Dawei Xie, Qiang Pan, Harold I. Feldman and Wensheng Guo
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Wei Yang: University of Pennsylvania School of Medicine
Dawei Xie: University of Pennsylvania School of Medicine
Qiang Pan: University of Pennsylvania School of Medicine
Harold I. Feldman: University of Pennsylvania School of Medicine
Wensheng Guo: University of Pennsylvania School of Medicine

Statistics in Biosciences, 2017, vol. 9, issue 2, No 11, 504-524

Abstract: Abstract We are motivated by the chronic renal insufficiency cohort (CRIC) study to identify risk factors for renal progression in patients with chronic kidney diseases. The CRIC study collects two types of renal outcomes: glomerular filtration rate (GFR) estimated annually and end-stage renal disease (ESRD). A related outcome of interest is death which is a competing event for ESRD. A joint modeling approach is proposed to model a longitudinal outcome and two competing survival outcomes. We assume multivariate normality on the joint distribution of the longitudinal and survival outcomes. Specifically, a mixed effects model is fit on the longitudinal outcome and a linear model is fit on each survival outcome. The three models are linked together by having the random terms of the mixed effects model as covariates in the survival models. EM algorithm is used to estimate the model parameters, and the nonparametric bootstrap is used for variance estimation. A simulation study is designed to compare the proposed method with an approach that models the outcomes sequentially in two steps. We fit the proposed model to the CRIC data and show that the protein-to-creatinine ratio is strongly predictive of both estimated GFR and ESRD but not death.

Keywords: Joint modeling; Multivariate normality; Informative dropout; Competing risk; Chronic kidney disease (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s12561-016-9186-4

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