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

PSC In The News

RSS Feed icon

Lam looks at population and development in next 15 years in UN commission keynote address

Mitchell et al. find harsh family environments may magnify disadvantage via impact on 'genetic architecture'

Frey says Arizona's political paradoxes explained in part by demography

Highlights

Raghunathan appointed director of Survey Research Center

PSC newsletter spring 2014 issue now available

Kusunoki wins faculty seed grant award from Institute for Research on Women and Gender

2014 PAA Annual Meeting, May 1-3, Boston

Next Brown Bag

Monday, April 21
Grant Miller: Managerial Incentives in Public Service Delivery

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