期刊
STATISTICS IN MEDICINE
卷 33, 期 8, 页码 1409-1425出版社
WILEY
DOI: 10.1002/sim.6043
关键词
confounding; competing risks; time-dependent confounding; failure-time data; survival analysis; simulation; multiple-outcome data; longitudinal data; marginal structural models; inverse-probability weighting; intermediate variables; causal inference
类别
资金
- Natural Sciences and Engineering Research Council (NSERC) of Canada
- Chercheur-Boursier career award from the Fonds de recherche en sante du Quebec (FRSQ)
- Chercheur-National career award from the FRSQ
- Reseau SIDA/maladies infectieuses of the FRSQ
- Canadian Institutes of Health Research (CIHR) [MOP-79529]
- CIHR Canadian HIV Trials Network [CTN222]
It is often the case that interest lies in the effect of an exposure on each of several distinct event types. For example, we are motivated to investigate in the impact of recent injection drug use on deaths due to each of cancer, end-stage liver disease, and overdose in the Canadian Co-infection Cohort (CCC). We develop a marginal structural model that permits estimation of cause-specific hazards in situations where more than one cause of death is of interest. Marginal structural models allow for the causal effect of treatment on outcome to be estimated using inverse-probability weighting under the assumption of no unmeasured confounding; these models are particularly useful in the presence of time-varying confounding variables, which may also mediate the effect of exposures. An asymptotic variance estimator is derived, and a cumulative incidence function estimator is given. We compare the performance of the proposed marginal structural model for multiple-outcome data to that of conventional competing risks models in simulated data and demonstrate the use of the proposed approach in the CCC. Copyright (c) 2013 John Wiley & Sons, Ltd.
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