Controlling Time-Dependent Confounding by Health Status and Frailty: Restriction Versus Statistical Adjustment

Nonexperimental studies of preventive interventions are often biased because of the healthy-user effect and, in frail populations, because of confounding by functional status. Bias is evident when estimating influenza vaccine effectiveness, even after adjustment for claims-based indicators of illnes...

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Bibliographic Details
Published in:American journal of epidemiology 2015-07, Vol.182 (1), p.17-25
Main Authors: McGrath, Leah J, Ellis, Alan R, Brookhart, M Alan
Format: Article
Language:English
Subjects:
Aged
Bias
Cohort Studies
Confounding Factors (Epidemiology)
Disease control
Epidemiology
Female
Health Status
Hemodialysis
Humans
Influenza
Influenza Vaccines
Kidney Failure, Chronic
Male
Middle Aged
Mortality
Practice of Epidemiology
Proportional Hazards Models
Vaccines
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Summary:Nonexperimental studies of preventive interventions are often biased because of the healthy-user effect and, in frail populations, because of confounding by functional status. Bias is evident when estimating influenza vaccine effectiveness, even after adjustment for claims-based indicators of illness. We explored bias reduction methods while estimating vaccine effectiveness in a cohort of adult hemodialysis patients. Using the United States Renal Data System and linked data from a commercial dialysis provider, we estimated vaccine effectiveness using a Cox proportional hazards marginal structural model of all-cause mortality before and during 3 influenza seasons in 2005/2006 through 2007/2008. To improve confounding control, we added frailty indicators to the model, measured time-varying confounders at different time intervals, and restricted the sample in multiple ways. Crude and baseline-adjusted marginal structural models remained strongly biased. Restricting to a healthier population removed some unmeasured confounding; however, this reduced the sample size, resulting in wide confidence intervals. We estimated an influenza vaccine effectiveness of 9% (hazard ratio = 0.91, 95% confidence interval: 0.72, 1.15) when bias was minimized through cohort restriction. In this study, the healthy-user bias could not be controlled through statistical adjustment; however, sample restriction reduced much of the bias.
ISSN:0002-9262
1476-6256
DOI:10.1093/aje/kwu485