Background It is widely acknowledged that the obesity epidemic exhibits many characteristics of a complex system – individual heterogeneity and autonomy, interdependence, adaptivity, feedback loops, threshold effects, and emergence. Altering patterns of obesity requires robust methods that are capable of accurately estimating the causal effects of potential interventions in the presence of such complexity. Two groups of methods that have been proposed are of interest: (1) statistical regression models informed by directed acyclic graphs (DAGs), underpinned by graphical model theory; and (2) individual-based simulation models (IBSMs), consisting of microsimulation models (MSMs) and agent-based models (ABMs). Both groups of Methods may be used to evaluate causal effects as counterfactual contrasts, but methodological work seeking to compare and contrast the two methods is lacking, because they have only come to prominence relatively recently and have been largely confined to separate research disciplines. We sought to examine how these methods have been used within obesity research, identify the primary philosophical and methodological differences between them, and identify any implications for future causal analyses.
Methods We conducted searches of Medline, EMBASE, and Scopus to identify articles (from 1996–2016) that sought to make causal inferences relating to obesity, weight, or other closely associated health-related behaviours (e.g. exercise) by utilising either DAG-based statistical methods or IBSMs.
Results Our search returned 38 relevant articles utilising DAG-based statistical methods and 45 relevant articles utilising IBSMs (31 MSMs and 14 ABMs). From these results, we identified four primary differences between the two groups of Methods: (1) their relative reliance on theory versus data; (2) the timescales upon which they operate; (3) their relative focus on fixed versus random effects; and (4) the ways in which they assess the effects of (hypothetical) interventions.
Conclusion The results of our search cast light on how the two groups of methods might be used as complementary strategies, as well as the circumstances under which their causal Conclusions might diverge. DAG-based analyses, whilst mathematically robust, are not well-suited to scenarios in which data do not exist or are hard to measure, where the exposures/interventions of interest ‘spillover’ amongst individuals, and random effects are of specific interest. IBSMs, in contrast, are flexible enough to handle such complexity, though the robustness of their causal conclusions is in contention. Greater integration of the methods should be sought, as it has the potential to aid causal inferences in complex systems; to this end, further methodological work is needed.
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