Premium
Generalizing evidence from randomized trials using inverse probability of sampling weights
Author(s) -
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.
Publication year - 2018
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12357
Subject(s) - estimator , mathematics , sampling (signal processing) , statistics , randomized controlled trial , population , stratified sampling , variance (accounting) , average treatment effect , computer science , medicine , surgery , environmental health , filter (signal processing) , computer vision , accounting , business
Summary 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.