  Comparative effectiveness evidence from randomized trials may not be directly
generalizable to a target population of substantive interest when, as in most
cases, trial participants are not randomly sampled from the target population.
Motivated by the need to generalize evidence from two trials conducted in the
AIDS Clinical Trials Group (ACTG), we consider weighting, regression and doubly
robust estimators to estimate the causal effects of HIV interventions in a
specified population of people living with HIV in the USA. We focus on a
non-nested trial design and discuss strategies for both point and variance
estimation of the target population average treatment effect. Specifically in
the generalizability context, we demonstrate both analytically and empirically
that estimating the known propensity score in trials does not increase the
variance for each of the weighting, regression and doubly robust estimators. We
apply these methods to generalize the average treatment effects from two ACTG
trials to specified target populations and operationalize key practical
considerations. Finally, we report on a simulation study that investigates the
finite-sample operating characteristics of the generalizability estimators and
their sandwich variance estimators.
