This paper describes the use of inverse probability of sampling weights which are used to adjust the results of clinical trials to generalize to a given population of interest.

Results obtained in randomized trials may not easily generalize to target populations. Whereas in randomized
trials the treatment assignment mechanism is known, the sampling mechanism by which individuals
are selected to participate in the trial is typically not known and assuming random sampling from
the target population is often dubious. We consider an inverse probability of sampling weighted (IPSW)
estimator for generalizing trial results to a target population. The IPSW estimator is shown to be consistent
and asymptotically normal. A consistent sandwich-type variance estimator is derived and simulation
results are presented comparing the IPSW estimator to a previously proposed stratified estimator. The
methods are then utilized to generalize results from two randomized trials of HIV treatment to all people
living with HIV in the US.

Generalizing Evidence from Randomized Trials using Inverse Probability of Sampling Weights