It is often of interest to investigators in the health sciences to examine the extent to which the effect of a treatment on some outcome is mediated by an intermediate variable. Various statistical techniques are often employed to carry out this mediation analysis. However, in order to interpret the results of such statistical analysis causally, that is, as direct and indirect effects of a particular treatment, certain strong assumptions must be made about confounding. The nounmeasured- confounding assumptions required in mediation analysis are stronger than those required in studies of the total effect of a treatment. In order to estimate controlled direct effects there must not only be no unmeasured confounders of the treatment-outcome relationship but there also must be no unmeasured confounders of the mediator-outcome relationship. Furthermore, for the decomposition of a total effect into a direct and an indirect effect, one must also assume that there are no unmeasured confounders of the treatment-mediator relationship and that there is no consequence of treatment that confounds the mediator-outcome relationship. In many observational studies these no-unmeasured-confounding assumptions will not hold. The purpose of this research is to obtain and apply results concerning bounds for direct and indirect effects in observational studies when the no-unmeasured-confounding assumptions required for the identification fail. Progress will be made principally by imposing monotonicity assumptions on the relationship between the unmeasured confounding variables and the treatment, mediator and outcome of interest. The results will be applied to a problem in perinatal epidemiology. Over the last twenty, utilization of prenatal care has increased in the United States;preterm birth rates, however, have also increased;c-section rates have increased;infant mortality has decreased. One possible explanation for these trends is that prenatal care allows for the early detection of pregnancy problems for which the appropriate response is preterm c-section or medically-induced labor. This would result in a greater proportion of preterm births but ultimately healthier infants. The research will examine the extent to which the effect of prenatal care on infant mortality is mediated by medically induced preterm birth. The no-unmeasured-confounding assumptions required for such an analysis are unlikely met in the National Center for Health Statistics Linked Birth Certificate and Infant Mortality files that will be analyzed and thus the results derived in the methodological research will be employed to determine bounds for the portion of the effect of prenatal care on infant mortality that is mediated by medically-induced preterm birth.

Public Health Relevance

The statistical methodology developed in the proposed research will be useful in studies assessing the extent to which the effect of one variable on another is mediated by some intermediate. The methods will be employed to determine the extent to which prenatal care affects infant mortality by allowing for the early detection of pregnancy problems for which the appropriate response is a preterm c-section or induced labor.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Small Research Grants (R03)
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Pediatrics Subcommittee (CHHD)
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Signore, Caroline
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Harvard University
Public Health & Prev Medicine
Schools of Public Health
United States
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VanderWeele, Tyler J; Lauderdale, Diane S; Lantos, John D (2013) Medically induced preterm birth and the associations between prenatal care and infant mortality. Ann Epidemiol 23:435-40
VanderWeele, Tyler J (2013) A three-way decomposition of a total effect into direct, indirect, and interactive effects. Epidemiology 24:224-32
VanderWeele, Tyler J (2013) Policy-relevant proportions for direct effects. Epidemiology 24:175-6
Wang, Peigang; Wang, Xiao; Fang, Min et al. (2013) Factors influencing the decision to participate in medical premarital examinations in Hubei Province, Mid-China. BMC Public Health 13:217
VanderWeele, Tyler J; Hernan, Miguel A (2013) Causal Inference Under Multiple Versions of Treatment. J Causal Inference 1:1-20
Valeri, Linda; Vanderweele, Tyler J (2013) Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods 18:137-50
Vanderweele, Tyler J; Hong, Guanglei; Jones, Stephanie M et al. (2013) Mediation and spillover effects in group-randomized trials: a case study of the 4Rs educational intervention. J Am Stat Assoc 108:469-482
Vanderweele, Tyler J (2012) Mediation analysis with multiple versions of the mediator. Epidemiology 23:454-63
VanderWeele, Tyler J; Hawkley, Louise C; Cacioppo, John T (2012) On the reciprocal association between loneliness and subjective well-being. Am J Epidemiol 176:777-84
Vanderweele, Tyler J; Tchetgen Tchetgen, Eric J; Halloran, M Elizabeth (2012) Components of the indirect effect in vaccine trials: identification of contagion and infectiousness effects. Epidemiology 23:751-61

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