Time-Dependent Propensity Score for Assessing the Effect of Vaccine Exposure on Pregnancy Outcomes through Pregnancy Exposure Cohort Studies

Ronghui Xu, Yunjun Luo, Robert Glynn, Diana Johnson, Kenneth L. Jones and Christina Chambers
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Ronghui Xu: Department of Family and Preventive Medicine, University of California, San Diego, 9500 Gilman Drive, MC 0112, La Jolla, CA 92093, USA
Yunjun Luo: Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
Robert Glynn: Department of Medicine, Brigham & Women's Hospital and Harvard Medical School, 1620 Tremont Street, Boston, MA 02120, USA
Diana Johnson: Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
Kenneth L. Jones: Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
Christina Chambers: Department of Family and Preventive Medicine, University of California, San Diego, 9500 Gilman Drive, MC 0112, La Jolla, CA 92093, USA

IJERPH, 2014, vol. 11, issue 3, 1-12

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.

Keywords: Cox regression; pH1N1 influenza vaccine; preterm delivery (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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