Longitudinal data are increasingly available to health researchers; these present challenges not encountered in cross-sectional data, not the least of which is the presence of time-varying confounding variables and intermediate effects.
We review confounding and mediation in a longitudinal setting and introduce causal graphs to explain the bias that arises from conventional analyses.
When both time-varying confounding and mediation are present in the data, traditional regression models result in estimates of effect coefficients that are systematically incorrect, or biased. In a companion paper (Moodie and Stephens in Int J Publ Health, 2010b, this issue), we describe a class of models that yield unbiased estimates in a longitudinal setting.
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Bodnar LM, Davidian M, Siega-Riz AM, Tsiatis A (2004) Marginal structural models for analyzing causal effects of time-dependent treatments: an application in perinatal epidemiology. Am J Epidemiol 159:926–934
Brotman RM, Klebanoff MA, Nansel TR, Andrews WW, Schwebke JR, Zhang J, Yu KF, Zenilman JM, Scharfstein DO (2008) A longitudinal study of vaginal douching and bacterial vaginosis a marginal structural modeling analysis. Am J Epidemiol 168:188–196
Greenland S, Pearl J, Robins JM (1999) Causal diagrams for epidemiologic research. Epidemiology 10:37–48
Moodie EEM, Stephens DA (2010a) Estimation of dose-response functions for longitudinal data using the generalized propensity score. Stat Methods Med Res. doi:10.1177/0962280209340213
Moodie EEM, Stephens DA (2010b) Marginal structural models: unbiased estimation for longitudinal studies. Int J Publ Health (this issue)
Pearl J (2009) Causality, 2nd edn. Cambridge University Press, London
Stewart CE, Fielder AR, Stephens DA, Moseley MJ (2002) Design of the Monitored Occlusion Treatment of Amblyopia Study (MOTAS). Br J Ophthalmol 86:915–919
Stewart CE, Moseley MJ, Stephens DA, Fielder AR (2004) Treatment dose-response in amblyopia therapy: the Monitored Occlusion Treatment of Amblyopia Study (MOTAS). Investig Ophthalmol Vis Sci 45:3048–3054
Both authors acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC). Moodie also acknowledges funding from the Canadian Institutes of Health Research (CIHR).
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Moodie, E.E.M., Stephens, D.A. Using Directed Acyclic Graphs to detect limitations of traditional regression in longitudinal studies. Int J Public Health 55, 701–703 (2010). https://doi.org/10.1007/s00038-010-0184-x
- Directed Acyclic Graphs
- Longitudinal data