Generalizing evidence from randomized trials using inverse probability of sampling weights

• Published in: Journal of the Royal Statistical Society. Series A, Statistics in society Vol. 181; no. 4; pp. 1193 - 1209
• Main Authors: Buchanan, Ashley L., Hudgens, Michael G., Cole, Stephen R., Mollan, Katie R., Sax, Paul E., Daar, Eric S., Adimora, Adaora A., Eron, Joseph J., Mugavero, Michael J.
• Format: Journal Article
• Language: English
• Published: England Wiley 01.10.2018; Oxford University Press
• Subjects: Causal inference; External validity–generalizability; HIV; Human immunodeficiency virus; Human immunodeficiency virus–acquired immune deficiency syndrome; Inverse probability weights; Probability; Random sampling; Randomization; Randomized controlled trial; Simulation; Target population; Viruses; Causal inference; HIV/AIDS; Inverse probability weights; Target population; External validity/Generalizability; Randomized controlled trial
• ISSN: 0964-1998; 1467-985X
• DOI: 10.1111/rssa.12357

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 with a previously proposed stratified estimator. The methods are then utilized to generalize results from two randomized trials of human immunodeficiency virus treatment to all people living with the disease in the USA.