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

PSC In The News

RSS Feed icon

Murphy on extending health support via a smart phone and JITAI

New analysis counters Shaefer's finding that households living on $2/day/person rose post welfare reform

Former trainee Herbert says residential squatters may be a good thing

More News

Highlights

Michigan ranked #12 on Business Insider's list of 50 best American colleges

Frey's new report explores how the changing US electorate could shape the next 5 presidential elections, 2016 to 2032

U-M's Data Science Initiative offers expanded consulting services via CSCAR

Elizabeth Bruch promoted to Associate Professor

Next Brown Bag

PSC Brown Bags
will resume fall 2016

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

Publication Abstract

Bray, B.C., Daniel 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