  Causally interpretable meta-analysis combines information from a collection
of randomized controlled trials to estimate treatment effects in a target
population in which experimentation may not be possible but covariate
information can be collected from a simple random sample. In such analyses, a
key practical challenge is systematically missing data when some baseline
covariates are not collected in all trials. Here, we provide identification
results for potential (counterfactual) outcome means and average treatment
effects in the target population when covariate data are systematically missing
from some of the trials in the meta-analysis. We propose three estimators for
the average treatment effect in the target population, examine their asymptotic
properties, and show that they have good finite-sample performance in
simulation studies. We use the estimators to analyze data from two large lung
cancer screening trials and target population data from the National Health and
Nutrition Examination Survey (NHANES). To accommodate the complex survey design
of the NHANES, we modify the methods to incorporate survey sampling weights and
allow for clustering.
