Assessing the total effect of time-varying predictors in prevention research

Bethany Cara Bray, Daniel Almirall, Rick S. Zimmerman, Donald Lynam, Susan A. Murphy

    Research output: Contribution to journalArticlepeer-review

    18 Scopus citations

    Abstract

    Observational data are often used to address prevention questions such as, "If alcohol initiation could be delayed, would that in turn cause a delay in marijuana initiation?" This question is concerned with the total causal effect of the timing of alcohol initiation on the timing of marijuana initiation. Unfortunately, when observational data are used to address a question such as the above, alternative explanations for the observed relationship between the predictor, here timing of alcohol initiation, and the response abound. These alternative explanations are due to the presence of confounders. Adjusting for confounders when using observational data is a particularly challenging problem when the predictor and confounders are time-varying. When time-varying confounders are present, the standard method of adjusting for confounders may fail to reduce bias and indeed can increase bias. In this paper, an intuitive and accessible graphical approach is used to illustrate how the standard method of controlling for confounders may result in biased total causal effect estimates. The graphical approach also provides an intuitive justification for an alternate method proposed by James Robins [Robins, J. M. (1998). 1997 Proceedings of the American Statistical Association, section on Bayesian statistical science (pp. 1-10). Retrieved from http://www.biostat.harvard.edu/robins/research.html; Robins, J. M., Hernán, M., & Brumback, B. (2000). Epidemiology, 11(5), 550-560]. The above two methods are illustrated by addressing the motivating question. Implications for prevention researchers who wish to estimate total causal effects using longitudinal observational data are discussed.

    Original languageEnglish (US)
    Pages (from-to)1-17
    Number of pages17
    JournalPrevention Science
    Volume7
    Issue number1
    DOIs
    StatePublished - Mar 2006

    All Science Journal Classification (ASJC) codes

    • Public Health, Environmental and Occupational Health

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