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. 2012 Nov;22(11):799-806.
doi: 10.1016/j.annepidem.2012.09.003. Epub 2012 Oct 5.

Correcting for exposure misclassification using survival analysis with a time-varying exposure

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Correcting for exposure misclassification using survival analysis with a time-varying exposure

Katherine Ahrens et al. Ann Epidemiol. 2012 Nov.

Abstract

Purpose: Survival analysis is increasingly being used in perinatal epidemiology to assess time-varying risk factors for various pregnancy outcomes. Here we show how quantitative correction for exposure misclassification can be applied to a Cox regression model with a time-varying dichotomous exposure.

Methods: We evaluated influenza vaccination during pregnancy in relation to preterm birth among 2267 non-malformed infants whose mothers were interviewed as part of the Slone Birth Defects Study during 2006 through 2011. The hazard of preterm birth was modeled using a time-varying exposure Cox regression model with gestational age as the time-scale. The effect of exposure misclassification was then modeled using a probabilistic bias analysis that incorporated vaccination date assignment. The parameters for the bias analysis were derived from both internal and external validation data.

Results: Correction for misclassification of prenatal influenza vaccination resulted in an adjusted hazard ratio (AHR) slightly higher and less precise than the conventional analysis: Bias-corrected AHR 1.04 (95% simulation interval, 0.70-1.52); conventional AHR, 1.00 (95% confidence interval, 0.71-1.41).

Conclusions: Probabilistic bias analysis allows epidemiologists to assess quantitatively the possible confounder-adjusted effect of misclassification of a time-varying exposure, in contrast with a speculative approach to understanding information bias.

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Figures

Figure 1
Figure 1
Distribution of influenza vaccinations during pregnancy by calendar week alongside modeled beta distribution (curved line) among women with first prenatal visit A) August through January and B) February through July.
Figure 2
Figure 2
Distribution of influenza vaccination dates, by calendar week, assigned during data simulation alongside modeled beta distribution (curved line) among women with first prenatal visit A) August through January and B) February through July.
Figure 3
Figure 3
Histograms of simulated adjusted hazard ratios (AHR) and median AHRs and 95% simulation intervals for the A) conventional and B) bias-corrected analyses of the association between influenza vaccination during pregnancy and preterm birth. The thick black line outlines the simulated conventional analysis AHR distribution. Models were adjusted for maternal race, age, infertility treatment, study center and multifetal gestation.

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