Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Smock cited in story on how low marriage rates may exacerbate marriage-status economic inequality

Shapiro says Americans' seemingly volatile spending pattern linked to 'sensible cash management'

Work of Cigolle, Ofstedal et al. cited in Forbes story on frailty risk among the elderly

Highlights

Sarah Burgard and former PSC trainee Jennifer Ailshire win ASA award for paper

James Jackson to be appointed to NSF's National Science Board

ISR's program in Society, Population, and Environment (SPE) focuses on social change and social issues worldwide.

McEniry and Schoeni host Conference on Long-run Impacts of Early Life Events

Next Brown Bag


PSC Brown Bags will return in the fall

Assessing the Total Effect of Time-Varying Predictors in Prevention Research

Publication Abstract

Bray, B.C., D. Almirall, R.S. Zimmerman, D. Lynam, and Susan A. Murphy. 2006. "Assessing the Total Effect of Time-Varying Predictors in Prevention Research." Prevention Science, 7(1): 1-17.

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., Hernan, 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.

DOI:10.1007/s11121-005-0023-0 (Full Text)

PMCID: PMC1479302. (Pub Med Central)

Browse | Search : All Pubs | Next