  Electronic health records are a valuable data source for investigating
health-related questions, and propensity score analysis has become an
increasingly popular approach to address confounding bias in such
investigations. However, because electronic health records are typically
routinely recorded as part of standard clinical care, there are often missing
values, particularly for potential confounders. In our motivating study --
using electronic health records to investigate the effect of renin-angiotensin
system blockers on the risk of acute kidney injury -- two key confounders,
ethnicity and chronic kidney disease stage, have $59\%$ and $53\%$ missing
data, respectively.
  The missingness pattern approach (MPA), a variant of the missing indicator
approach, has been proposed as a method for handling partially observed
confounders in propensity score analysis. In the MPA, propensity scores are
estimated separately for each missingness pattern present in the data. Although
the assumptions underlying the validity of the MPA are stated in the
literature, it can be difficult in practice to assess their plausibility.
  In this paper, we explore the MPA's underlying assumptions by using causal
diagrams to assess their plausibility in a range of simple scenarios, drawing
general conclusions about situations in which they are likely to be violated.
We present a framework providing practical guidance for assessing whether the
MPA's assumptions are plausible in a particular setting and thus deciding when
the MPA is appropriate. We apply our framework to our motivating study, showing
that the MPA's underlying assumptions appear reasonable, and we demonstrate the
application of MPA to this study.
