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Blood pressure and the risk of chronic kidney disease progression using multistate marginal structural models in the CRIC Study
Author(s) -
StephensShields Alisa J.,
Spieker Andrew J.,
Anderson Amanda,
Drawz Paul,
Fischer Michael,
Sozio Stephen M.,
Feldman Harold,
Joffe Marshall,
Yang Wei,
Greene Tom
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7425
Subject(s) - medicine , confounding , kidney disease , renal function , marginal structural model , blood pressure , disease , observational study , population , censoring (clinical trials) , proportional hazards model , creatinine , cardiology , intensive care medicine , pathology , environmental health
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.