How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies

How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies

Abstract Background Analysis of competing risks is commonly achieved through a cause specific or a subdistribution framework using Cox or Fine & Gray models, respectively. The estimation of treatment effects in observational data is prone to unmeasured confounding which causes bias. There has be...

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Bibliographic Details
Main Authors: Michael Andrew Barrowman, Niels Peek, Mark Lambie, Glen Philip Martin, Matthew Sperrin
Format: Article
Language:English
Published: BMC 2019-07-01
Series:BMC Medical Research Methodology
Subjects:
Competing risks
Unmeasured confounding
Simulation study
Observation studies
Online Access:http://link.springer.com/article/10.1186/s12874-019-0808-7

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http://link.springer.com/article/10.1186/s12874-019-0808-7

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