Article
Adaptive pair-matching in randomized trials with unbiased and efficient effect estimation
Statistics in Medicine
(2015)
Abstract
In randomized trials, pair-matching is an intuitive design strategy to protect study validity and to potentially
increase study power. In a common design, candidate units are identified, and their baseline characteristics used
to create the best n∕2 matched pairs.Within the resulting pairs, the intervention is randomized, and the outcomes
measured at the end of follow-up.We consider this design to be adaptive, because the construction of thematched
pairs depends on the baseline covariates of all candidate units. As a consequence, the observed data cannot be
considered as n∕2 independent, identically distributed pairs of units, as common practice assumes. Instead, the
observed data consist of n dependent units. This paper explores the consequences of adaptive pair-matching in
randomized trials for estimation of the average treatment effect, conditional the baseline covariates of the n study
units. By avoiding estimation of the covariate distribution, estimators of this conditional effect will often be more
precise than estimators of the marginal effect.We contrast the unadjusted estimator with targeted minimum loss
based estimation and show substantial efficiency gains from matching and further gains with adjustment. This
work is motivated by the Sustainable East Africa Research in Community Health study, an ongoing community
randomized trial to evaluate the impact of immediate and streamlined antiretroviral therapy on HIV incidence
in rural East Africa.
Disciplines
Publication Date
2015
DOI
10.1002/sim.6380
Citation Information
Laura Balzer, M Petersen, M van der Laan and the SEARCH Consortium. "Adaptive pair-matching in randomized trials with unbiased and efficient effect estimation" Statistics in Medicine Vol. 34 (2015) p. 999 - 1011 Available at: http://works.bepress.com/laura_balzer/29/