Blood pressure and the risk of chronic kidney disease progression using multistate marginal structural models in the CRIC Study

• Published in: Statistics in medicine Vol. 36; no. 26; pp. 4167 - 4181
• Main Authors: Stephens‐Shields, Alisa J., Spieker, Andrew J., Anderson, Amanda, Drawz, Paul, Fischer, Michael, Sozio, Stephen M., Feldman, Harold, Joffe, Marshall, Yang, Wei, Greene, Tom
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 20.11.2017
• Subjects: Blood Pressure; causal inference; Causality; Cohort Studies; Computer Simulation; Confounding Factors, Epidemiologic; Disease Progression; Glomerular Filtration Rate; Humans; inverse probability weighting; Kidney diseases; Markov Chains; Medical statistics; Models, Statistical; multistate models; Probability; renal disease progression; Renal Insufficiency, Chronic; Risk Factors; inverse probability weighting; multistate models; renal disease progression; causal inference
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.7425

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.