An important consideration in clinical research studies is proper evaluation
of internal and external validity. While randomized clinical trials can
overcome several threats to internal validity, they may be prone to poor
external validity. Conversely, large prospective observational studies sampled
from a broadly generalizable population may be externally valid, yet
susceptible to threats to internal validity, particularly confounding. Thus,
methods that address confounding and enhance transportability of study results
across populations are essential for internally and externally valid causal
inference, respectively. We develop a weighting method which estimates the
effect of an intervention on an outcome in an observational study which can
then be transported to a second, possibly unrelated target population. The
proposed methodology employs calibration estimators to generate complementary
balancing and sampling weights to address confounding and transportability,
respectively, enabling valid estimation of the target population average
treatment effect. A simulation study is conducted to demonstrate the advantages
and similarities of the calibration approach against alternative techniques. We
also test the performance of the calibration estimator-based inference in a
motivating real data example comparing whether the effect of biguanides versus
sulfonylureas - the two most common oral diabetes medication classes for
initial treatment - on all-cause mortality described in a historical cohort
applies to a contemporary cohort of US Veterans with diabetes