title: Time-dependent propensity score for assessing the effect of vaccine exposure on pregnancy outcomes through pregnancy exposure cohort studies.
abstract: Women are advised to be vaccinated for influenza during pregnancy and may receive vaccine at any time during their pregnancy. In observational studies evaluating vaccine safety in pregnancy, to account for such time-varying vaccine exposure, a time-dependent predictor can be used in a proportional hazards model setting for outcomes such as spontaneous abortion or preterm delivery. Also, due to the observational nature of pregnancy exposure cohort studies and relatively low event rates, propensity score (PS) methods are often used to adjust for potential confounders. Using Monte Carlo simulation experiments, we compare two different ways to model the PS for vaccine exposure: (1) logistic regression treating the exposure status as binary yes or no; (2) Cox regression treating time to exposure as time-to-event. Coverage probability of the nominal 95% confidence interval for the exposure effect is used as the main measure of performance. The performance of the logistic regression PS depends largely on how the exposure data is generated. In contrast, the Cox regression PS consistently performs well across the different data generating mechanisms that we have considered. In addition, the Cox regression PS allows adjusting for potential time-varying confounders such as season of the year or exposure to additional vaccines. The application of the Cox regression PS is illustrated using data from a recent study of the safety of pandemic H1N1 influenza vaccine during pregnancy.
authors.affiliation: Department of Family and Preventive Medicine, University of California, San Diego, 9500 Gilman Drive, MC 0112, La Jolla, CA 92093, USA. rxu@ucsd.edu.
authors.forename: Ronghui
authors.initials: R
authors.lastname: Xu
authors.affiliation: Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. yuluo@ucsd.edu.
authors.forename: Yunjun
authors.initials: Y
authors.lastname: Luo
authors.affiliation: Department of Medicine, Brigham & Women's Hospital and Harvard Medical School, 1620 Tremont Street, Boston, MA 02120, USA. rglynn@partners.org.
authors.forename: Robert
authors.initials: R
authors.lastname: Glynn
authors.affiliation: Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. d4johnson@ucsd.edu.
authors.forename: Diana
authors.initials: D
authors.lastname: Johnson
authors.affiliation: Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. klyons@ucsd.edu.
authors.forename: Kenneth L
authors.initials: KL
authors.lastname: Jones
authors.affiliation: Department of Family and Preventive Medicine, University of California, San Diego, 9500 Gilman Drive, MC 0112, La Jolla, CA 92093, USA. chchambers@ucsd.edu.
authors.forename: Christina
authors.initials: C
authors.lastname: Chambers
mesh_terms: D015331:Cohort Studies; D005260:Female; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000276:immunology*; D007252:Influenza Vaccines / Q000009:adverse effects*; D016015:Logistic Models; D009010:Monte Carlo Method; D011247:Pregnancy; D011256:Pregnancy Outcome; D057216:Propensity Score; D016016:Proportional Hazards Models
chemical_list: D007252:Influenza Vaccines