ArticlePrevention science : the official journal of the Society for Prevention Research2006
Assessing the total effect of time-varying predictors in prevention research.
Article in Prevention science : the official journal of the Society for Prevention Research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Assessing the Potential Cumulative Effects of Longitudinal Interventions for Depression and Deviant Peer Association on Academic Achievement: a Causal Inference Approach.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Maternal postnatal depression and sensitive parenting: Considering developmental timing with a causally informative approach.Developmental psychology · 2026Article
- Inverse Propensity Score Weighting with a Latent Class Exposure: Estimating the Causal Effect of Reported Reasons for Alcohol Use on Problem Alcohol Use 16 Years Later.Prevention science : the official journal of the Society for Prevention Research · 2019Article
- Examination of cumulative effects of early adolescent depression on cannabis and alcohol use disorder in late adolescence in a community-based cohort.Addiction (Abingdon, England) · 2017Article
- Circulating Cholesterol Levels May Link to the Factors Influencing Parkinson's Risk.Frontiers in neurology · 2017Article
- Causal inference and longitudinal data: a case study of religion and mental health.Social psychiatry and psychiatric epidemiology · 2016Review
- Gang membership and substance use: guilt as a gendered causal pathway.Journal of experimental criminology · 2015Article
- Subgroups analysis when treatment and moderators are time-varying.Prevention science : the official journal of the Society for Prevention Research · 2013Article
- Assessing mediation using marginal structural models in the presence of confounding and moderation.Psychological methods · 2012Article
- On the reciprocal association between loneliness and subjective well-being.American journal of epidemiology · 2012Article
- Invited commentary: structural equation models and epidemiologic analysis.American journal of epidemiology · 2012Article
- Introducing the at-risk average causal effect with application to HealthWise South Africa.Prevention science : the official journal of the Society for Prevention Research · 2012Article
- A marginal structural model analysis for loneliness: implications for intervention trials and clinical practice.Journal of consulting and clinical psychology · 2011Article
- Investigating the impact of selection bias in dose-response analyses of preventive interventions.Prevention science : the official journal of the Society for Prevention Research · 2010Article
- Drinking frequency and quantity and risk of suicide among men.Social psychiatry and psychiatric epidemiology · 2007Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.