title: Correcting for exposure misclassification using survival analysis with a time-varying exposure.
abstract: OBJECTIVE
  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.
authors.affiliation: Slone Epidemiology Center at Boston University, MA, USA.
authors.forename: Katherine
authors.initials: K
authors.lastname: Ahrens
authors.forename: Timothy L
authors.initials: TL
authors.lastname: Lash
authors.forename: Carol
authors.initials: C
authors.lastname: Louik
authors.forename: Allen A
authors.initials: AA
authors.lastname: Mitchell
authors.forename: Martha M
authors.initials: MM
authors.lastname: Werler
mesh_terms: D015982:Bias; D016001:Confidence Intervals; D005260:Female; D005865:Gestational Age; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature; D007252:Influenza Vaccines / Q000008:administration & dosage* / Q000276:immunology; D007251:Influenza, Human / Q000276:immunology / Q000517:prevention & control*; D009010:Monte Carlo Method; D011247:Pregnancy; D011256:Pregnancy Outcome / Q000453:epidemiology*; D047928:Premature Birth / Q000145:classification / Q000453:epidemiology*; D016016:Proportional Hazards Models; D012044:Regression Analysis; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors; D014611:Vaccination / Q000706:statistics & numerical data
chemical_list: D007252:Influenza Vaccines