Canonical Causal Diagrams to Guide the Treatment of Missing Data in Epidemiological Studies

With incomplete data, the missing at random (MAR) assumption is widely understood to enable unbiased estimation with appropriate methods. The need to assess the plausibility of MAR and to perform sensitivity analyses considering missing not at random (MNAR) scenarios have been emphasized, but the practical difficulty of these tasks is rarely acknowledged. What MAR means with multivariable missingness is difficult to grasp, while in many MNAR scenarios unbiased estimation is possible using methods commonly associated with MAR. Directed acyclic graphs (DAGs) have been proposed as an alternative framework for specifying practically accessible assumptions beyond the MAR-MNAR dichotomy. However, there is currently no general algorithm for deciding how to handle the missing data given a specific DAG. We construct "canonical" DAGs capturing typical missingness mechanisms in epidemiological studies with incomplete exposure, outcome and confounders. For each DAG, we determine whether common target parameters are "recoverable", meaning that they can be expressed as functions of the observed data distribution and thus estimated consistently, or if sensitivity analyses are necessary. We investigate the performance of available case and multiple imputation procedures. Using the Longitudinal Study of Australian Children, we illustrate how our findings can guide the treatment of missing data in point-exposure studies