CAPITULO II: APLICACIÓN DE LA PROPUESTA METODOLÓGICA
2.8 CONSTRUCCIÓN DE MATERIALES
The pivotal causal model posited in the data I-DAG is that, controlling for the baseline confounders, maternal drinking at adolescent age 12 has a causal effect on adolescent drinking at age 16.5. The data I-DAG further posits that much of this effect is through numerous intermediates between age 12 and 16.5, mainly: parenting; the parent child relationship; adolescent behaviours; adolescent mental health; peer effects; and institutional effects (school, neighbourhood). If estimating the direct effect of maternal drinking, none of the intermediates should be controlled for, including the parenting and
154 parent-child relationship variables. Similarly, indirect effects through any of the intermediates, including the parenting mediators, can be estimated by controlling the EIMOCs suggested by the data I-DAG.
As such the data I-DAG is compatible with SRO 4 and SRO 5, and the analysis plan. SRO 4 was to test average effects of the parental influences and their intermediates as determined by the data I- DAG. Chapter 3 determined that using inverse probability weighting (IPW) to estimate average causal effects (ACEs) is a suitable method for doing so. The role of the data I-DAG in this regard is simply to identify the variables that are to be controlled for when estimating the ACEs (i.e. the variables factored in the propensity scores for the IPWs). In practice this means controlling for every node with an input into the variable being assessed. For example, using DAGitty to select the exposure, the data I-DAG suggests that the effect of parenting and the parent-child relationship (measured at age 15.5) can be estimated by controlling for the baseline confounders, maternal drinking, parental permissiveness towards adolescent alcohol use, and various non-parental intermediates. In contrast, the data I-DAG suggests that only the baseline confounders (and potentially maternal smoking and maternal mental health) need to be factored into the IPW for maternal drinking.
SRO 5 was to use the information from the average effects analysis to investigate mediators of parental influences. Now that the data I-DAG has identified maternal drinking as the exposure of interest, SRO 5 can be refined. Thus SRO 5.1 becomes ‘investigate indirect effects of maternal drinking via parenting and the parent-child relationship’; and SRO 5.2 becomes ‘investigate indirect effects of maternal drinking mediated via non-parental influences’. Note however that both SRO 5.1 and SRO 5.2 are dependent on the analysis of average effects. Chapter 3 selected mediational g- computation as a suitable method for investigating these effects, mainly due to its ability to account for EIMOCs. Essentially, when estimating indirect effects, the data I-DAG is used to identify the EIMOCs that need to be modelled for the mediator in question. For example, when estimating the indirect effect of maternal drinking through the parental monitoring measured at age 15.5, the data I- DAG suggests identifying every intermediate measured between age 12 and 15.5 as a potential EIMOC (the simplified data I-DAG in Figure 7-4 is set to this specific question). As such the mediation analyses corresponding to SRO 5.1 may be assumed to deal with the issue of exposure- induced mediator-outcome confounding in a comprehensive fashion. However, due to the timing of the data, some of the intermediates measured at earlier ages cannot be said to account for EIMOCs as comprehensively, including the effect of parental permissiveness towards adolescent alcohol use measured at age 12.5. This is a limitation discussed further in Chapter 10.
155 As noted in Chapter 2 and Chapter 3, when researchers are less certain about the presence or
directionality of edges in a DAG, they should be free to transparently and systematically manipulate these edges in an attempt to gain some indication of their effect. While the restrictions imposed by temporal ordering between the surveys in ALSPAC arguably preclude this type of sensitivity analysis between timepoints, the cross-sectional groupings are entirely compatible with this approach. This is noted when relevant over the subsequent analyses.
7.6 Conclusion
This and the previous two chapters have met MRO 3 – apply and demonstrate ESC-DAGs. The process of developing the data I-DAG has been detailed from the earliest steps. The review of systematic reviews was used to determine the important parental influences on adolescent alcohol harm. The ESC-DAGs review then systematically extracted and inter-related these concepts through the Translation and Synthesis stages, thus producing the conceptual I-DAG. Finally, this chapter has demonstrated how the concepts in the conceptual I-DAG are replaced by variables from a data source, and reflected on some of the tensions between DAG-based approaches and real-world data. The above discussion also concluded that the data I-DAG is suitable to meet the analytical research objectives.
The data I-DAG is a complex diagram that integrates multiple components of the socio-ecological model of health. For example, the total number of causal paths in the data I-DAG, even with reducing all confounders to one node, is 382,507,508. As such, it is categorically more comprehensive than DAGs that are generally used in the literature. Additionally, by its nature the data I-DAG can readily direct causal analysis. Thus, it is the position of this chapter that the data I-DAG, as the final product of the ESC-DAGs process for this thesis, further supports the argument that ESC-DAGs meets MRO 2 – to develop a method for building DAGs (an argument made in Chapter 4 and supported by peer review).
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