A Comparison of Agent-Based Models and the Parametric G-Formula for Causal Inference.

ABSTRACT: Decision-making requires choosing from treatments on the basis of correctly estimated outcome distributions under each treatment. In the absence of randomized trials, 2 possible approaches are the parametric g-formula and agent-based models (ABMs). The g-formula has been used exclusively to estimate effects in the population from which data were collected, whereas ABMs are commonly used to estimate effects in multiple populations, necessitating stronger assumptions. Here, we describe potential biases that arise when ABM assumptions do not hold. To do so, we estimated 12-month mortality risk in simulated populations differing in prevalence of an unknown common cause of mortality and a time-varying confounder. The ABM and g-formula correctly estimated mortality and causal effects when all in

SUBMITTER: Murray EJ 

PROVIDER: S-EPMC5860229 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature