BACKGROUND: When assessing the association between a factor X and a subsequent outcome Y in observational studies, the question that arises is what are the variables to adjust for to reduce bias due to confounding for causal inference on the effect of X on Y. Disregarding such factors is often a source of overestimation because these variables may affect both X and Y. On the other hand, adjustment for such variables can also be a source of underestimation because such variables may be the causal consequence of X and part of the mechanism that leads from X to Y. METHODS: In this paper, we present a simple method to compute control variables in the presence of age at onset data on both X and a set of other variables. Using these age at onset data, control variables are computed that adjust only for conditions that occur prior to X. This strategy can be used in prospective as well as in survival analysis. Our method is motivated by an argument based on the counterfactual model of a causal effect. RESULTS: The procedure is exemplified by examining of the relation between panic attack and the subsequent incidence of MDD. CONCLUSIONS: The results reveal that the adjustment for all other variables, irrespective of their temporal relation to X, can yield a false negative result (despite unconsidered confounders and other sources of bias).
BACKGROUND: When assessing the association between a factor X and a subsequent outcome Y in observational studies, the question that arises is what are the variables to adjust for to reduce bias due to confounding for causal inference on the effect of X on Y. Disregarding such factors is often a source of overestimation because these variables may affect both X and Y. On the other hand, adjustment for such variables can also be a source of underestimation because such variables may be the causal consequence of X and part of the mechanism that leads from X to Y. METHODS: In this paper, we present a simple method to compute control variables in the presence of age at onset data on both X and a set of other variables. Using these age at onset data, control variables are computed that adjust only for conditions that occur prior to X. This strategy can be used in prospective as well as in survival analysis. Our method is motivated by an argument based on the counterfactual model of a causal effect. RESULTS: The procedure is exemplified by examining of the relation between panic attack and the subsequent incidence of MDD. CONCLUSIONS: The results reveal that the adjustment for all other variables, irrespective of their temporal relation to X, can yield a false negative result (despite unconsidered confounders and other sources of bias).
Authors: Julia Trumpf; Jürgen Margraf; Noortje Vriends; Andrea H Meyer; Eni S Becker Journal: Soc Psychiatry Psychiatr Epidemiol Date: 2009-11-04 Impact factor: 4.328