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Statistical Methods for Modeling Time-Updated Exposures in Cohort Studies of Chronic Kidney Disease.

ABSTRACT: When estimating the effect of an exposure on a time-to-event type of outcome, one can focus on the baseline exposure or the time-updated exposures. Cox regression models can be used in both situations. When time-dependent confounding exists, the Cox model with time-updated covariates may produce biased effect estimates. Marginal structural models, estimated through inverse-probability weighting, were developed to appropriately adjust for time-dependent confounding. We review the concept of time-dependent confounding and illustrate the process of inverse-probability weighting. We fit a marginal structural model to estimate the effect of time-updated systolic BP on the time to renal events such as ESRD in the Chronic Renal Insufficiency Cohort. We compare the Cox regression model and the marginal structural model on several attributes (effects estimated, result interpretation, and assumptions) and give recommendations for when to use each method.

SUBMITTER: Xie D

PROVIDER: S-EPMC5672960 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Statistical Methods for Modeling Time-Updated Exposures in Cohort Studies of Chronic Kidney Disease.

Xie Dawei D Yang Wei W Jepson Christopher C Roy Jason J Hsu Jesse Y JY Shou Haochang H Anderson Amanda H AH Landis J Richard JR Feldman Harold I HI

Clinical journal of the American Society of Nephrology : CJASN 20170817 11

When estimating the effect of an exposure on a time-to-event type of outcome, one can focus on the baseline exposure or the time-updated exposures. Cox regression models can be used in both situations. When time-dependent confounding exists, the Cox model with time-updated covariates may produce biased effect estimates. Marginal structural models, estimated through inverse-probability weighting, were developed to appropriately adjust for time-dependent confounding. We review the concept of time- ...[more]

PMID: 28818846

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Project description:ObjectivesOccupational exposure assessment for population-based case-control studies is challenging due to the wide variety of industries and occupations encountered by study participants. We developed and evaluated statistical models to estimate the intensity of exposure to three chlorinated solvents-methylene chloride, 1,1,1-trichloroethane, and trichloroethylene-using a database of air measurement data and associated exposure determinants.MethodsA measurement database was developed after an extensive review of the published industrial hygiene literature. The database of nearly 3000 measurements or summary measurements included sample size, measurement characteristics (year, duration, and type), and several potential exposure determinants associated with the measurements: mechanism of release (e.g. evaporation), process condition, temperature, usage rate, type of ventilation, location, presence of a confined space, and proximity to the source. The natural log-transformed measurement levels in the exposure database were modeled as a function of the measurement characteristics and exposure determinants using maximum likelihood methods. Assuming a single lognormal distribution of the measurements, an arithmetic mean exposure intensity level was estimated for each unique combination of exposure determinants and decade.ResultsThe proportions of variability in the measurement data explained by the modeled measurement characteristics and exposure determinants were 36, 38, and 54% for methylene chloride, 1,1,1-trichloroethane, and trichloroethylene, respectively. Model parameter estimates for the exposure determinants were in the anticipated direction. Exposure intensity estimates were plausible and exhibited internal consistency, but the ability to evaluate validity was limited.ConclusionsThese prediction models can be used to estimate chlorinated solvent exposure intensity for jobs reported by population-based case-control study participants that have sufficiently detailed information regarding the exposure determinants.

Dataset Information

Statistical Methods for Modeling Time-Updated Exposures in Cohort Studies of Chronic Kidney Disease.

Publications

Statistical Methods for Modeling Time-Updated Exposures in Cohort Studies of Chronic Kidney Disease.

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