TY - JOUR
T1 - Assessing the total effect of time-varying predictors in prevention research
AU - Bray, Bethany Cara
AU - Almirall, Daniel
AU - Zimmerman, Rick S.
AU - Lynam, Donald
AU - Murphy, Susan A.
N1 - Funding Information:
Preparation of this paper was supported by grant P50-DA-10075 from the National Institute on Drug Abuse to the Methodology Center at the Pennsylvania State University, by grant T32-DA-017629 to the Methodology Center and the Prevention Research Center for the Promotion of Human Development at the Pennsylvania State University, and by the National Institute on Drug Abuse award K02-DA-15674-01.
PY - 2006/3
Y1 - 2006/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33646480615&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646480615&partnerID=8YFLogxK
U2 - 10.1007/s11121-005-0023-0
DO - 10.1007/s11121-005-0023-0
M3 - Article
C2 - 16489417
AN - SCOPUS:33646480615
SN - 1389-4986
VL - 7
SP - 1
EP - 17
JO - Prevention Science
JF - Prevention Science
IS - 1
ER -