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Sensitivity analysis for unmeasured confounding in a marginal structural Cox proportional hazards model
Ole Klungsøyr1, Joe Sexton, Inger Sandanger
1Akershus University Hospital, Helse Øst Health Services Research Centre, Nordbyhagen, Norway. ole.klungsoyr@medisin.uio.no
Lifetime Data Analysis
|December 26, 2008
Summary
Sensitivity analysis for unmeasured confounding is crucial in observational studies. A new Cox proportional hazards marginal structural model (MSM) offers simpler computations for assessing bias from unmeasured factors in survival data.
Area of Science:
- Epidemiology
- Biostatistics
- Observational Research Methods
Background:
- Sensitivity analysis for unmeasured confounding is vital in observational studies but often computationally challenging with standard Cox models.
- Existing methods for marginal structural models (MSMs) require complex adjustments, limiting their application.
Purpose of the Study:
- To present a simplified computational approach for sensitivity analysis of unmeasured confounding in Cox proportional hazards marginal structural models (MSMs) with point exposure.
- To demonstrate the utility of this method for survival time data.
Main Methods:
- Adapted the general framework for MSM sensitivity analysis to survival data.
- Developed a method to correct the hazard rate directly for unmeasured confounding, avoiding bias-corrected observations.
- Applied the Cox proportional hazards MSM to reanalyze the association between smoking and depression in a Norwegian adult cohort.
Main Results:
- The proposed method provides straightforward sensitivity analysis for unmeasured confounding in Cox proportional hazards MSMs with point exposure.
- The reanalysis of smoking and depression association indicated moderate sensitivity to unmeasured confounding.
- The Cox proportional hazards MSM demonstrated robustness against differential loss to follow-up.
Conclusions:
- The described computational approach simplifies sensitivity analysis for unmeasured confounding in Cox proportional hazards MSMs.
- This method enhances the reliability of findings from observational studies, particularly those with point exposures.
- The association between smoking and depression in the studied cohort is subject to moderate bias from unmeasured confounders.
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