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

PSC In The News

RSS Feed icon

Smock discusses the "new American family" on NPR

Pfeffer and colleagues re-examine impacts of community college attendance

Frey explains the minority-majority remapping of America

Highlights

Apply for 2-year NICHD Postdoctoral Fellowships that begin September 2015

PSC Fall 2014 Newsletter now available

Martha Bailey and Nicolas Duquette win Cole Prize for article on War on Poverty

Michigan's graduate sociology program tied for 4th with Stanford in USN&WR rankings

Next Brown Bag

Monday, Dec 1
Linda Waite, Health & Well-Being of Adults over 60

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