  Missing data is a pernicious problem in epidemiologic research. Research on
the validity of complete case analysis for missing data has typically focused
on estimating the average treatment effect (ATE) in the whole population.
However, other target populations like the treated (ATT) or external targets
can be of substantive interest. In such cases, whether missing covariate data
occurs within or outside the target population may impact the validity of
complete case analysis. We sought to assess bias in complete case analysis when
covariate data is missing outside the target (e.g., missing covariate data
among the untreated when estimating the ATT). We simulated a study of the
effect of a binary treatment X on a binary outcome Y in the presence of 3
confounders C1-C3 that modified the risk difference (RD). We induced
missingness in C1 only among the untreated under 4 scenarios: completely
randomly (similar to MCAR); randomly based on C2 and C3 (similar to MAR);
randomly based on C1 (similar to MNAR); or randomly based on Y (similar to
MAR). We estimated the ATE and ATT using weighting and averaged results across
the replicates. We conducted a parallel simulation transporting trial results
to a target population in the presence of missing covariate data in the trial.
In the complete case analysis, estimated ATE was unbiased only when C1 was MCAR
among the untreated. The estimated ATT, on the other hand, was unbiased in all
scenarios except when Y caused missingness. The parallel simulation of
generalizing and transporting trial results saw similar bias patterns. If
missing covariate data is only present outside the target population, complete
case analysis is unbiased except when missingness is associated with the
outcome.
