In patients with chronic kidney disease (CKD), clinical interest often centers on determining treatments and exposures that are causally related to renal progression. Analyses of longitudinal clinical data in this population are often complicated by clinical competing events, such as end-stage renal disease (ESRD) and death, and time-dependent confounding, where patient factors that are predictive of later exposures and outcomes are affected by past exposures. We developed multistate marginal structural models (MS-MSMs) to assess the effect of time-varying systolic blood pressure on disease progression in subjects with CKD. The multistate nature of the model allows us to jointly model disease progression characterized by changes in the estimated glomerular filtration rate (eGFR), the onset of ESRD, and death, and thereby avoid unnatural assumptions of death and ESRD as noninformative censoring events for subsequent changes in eGFR. We model the causal effect of systolic blood pressure on the probability of transitioning into 1 of 6 disease states given the current state. We use inverse probability weights with stabilization to account for potential time-varying confounders, including past eGFR, total protein, serum creatinine, and hemoglobin. We apply the model to data from the Chronic Renal Insufficiency Cohort Study, a multisite observational study of patients with CKD.
Bibliographical noteFunding Information:
Wei Yang and Tom Greene contributed equally to this manuscript. The CRIC Study Investigators are Lawrence J. Appel, MD, MPH; Alan S. Go, MD; Jiang He, MD, PhD; John W. Kusek, PhD; James P. Lash, MD; Akinlolu Ojo, MD, PhD; Mahboob Rahman, MD; and Raymond R. Townsend, MD. Funding information: National Institute of Diabetes and Digestive and Kidney Diseases, Grant/Award Number: R01 DK090046 ; Health Resources & Services Adminstration, Grant/Award Number: D34HP24459-01 ; National Institute of Diabetes and Digestive and Kidney Diseases, Grant/Award Number: U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, U01DK060902 ; Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award NIH/NCATS, Grant/Award Number: UL1TR000003; Johns Hopkins University, Grant/Award Number: UL1 TR-000424 ; University of Maryland GCRC, Grant/Award Number: M01 RR-16500 ; Clinical and Translational Science Collaborative of Cleveland, Grant/Award Number: UL1TR000439 ; National Center for Advancing Translational Sciences (NCATS) ; Michigan Institute for Clinical and Health Research (MICHR), Grant/Award Number: UL1TR000433 ; University of Illinois at Chicago CTSA, Grant/Award Number: UL1RR029879 ; Tulane University Translational Research in Hypertension and Renal Biology, Grant/Award Number: P30GM103337; Kaiser Permanente NIH/NCRR UCSF-CTSI, Grant/Award Number: UL1 RR-024131
This work was funded by National Institute of Diabetes and Digestive and Kidney Diseases grant R01 DK090046. Dr Stephens-Shields and Dr Spieker were additionally supported by the Health Resources & Services Adminstration grant D34HP24459-01. Funding for the CRIC Study was obtained under a cooperative agreement from National Institute of Diabetes and Digestive and Kidney Diseases (U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, and U01DK060902). In addition, the CRIC Study was also supported in part by the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award NIH/NCATSUL1TR000003, Johns Hopkins UniversityUL1 TR-000424, University of Maryland GCRCM01 RR-16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research (MICHR)UL1TR000433, University of Illinois at Chicago CTSAUL1RR029879, Tulane University Translational Research in Hypertension and Renal BiologyP30GM103337, Kaiser Permanente NIH/NCRR UCSF-CTSIUL1 RR-024131. The authors would like to thank Bo Hu and Liang Li for insightful discussions.
- causal inference
- inverse probability weighting
- multistate models
- renal disease progression