  Two important considerations in clinical research studies are proper
evaluations 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. These issues persist for another problem closely
related to transportability known as data-fusion. We develop a calibration
method to generate balancing weights that address confounding and sampling
bias, thereby enabling valid estimation of the target population average
treatment effect. We compare the calibration approach to two additional
doubly-robust methods that estimate the effect of an intervention on an outcome
within a second, possibly unrelated target population. The proposed
methodologies can be extended to resolve data-fusion problems that seek to
evaluate the effects of an intervention using data from two related studies
sampled from different populations. A simulation study is conducted to
demonstrate the advantages and similarities of the different techniques. We
also test the performance of the calibration approach 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.
