Article
Targeted estimation and inference for the sample average treatment effect in trials with and without pair-matching
Statistics in Medicine
(2016)
Abstract
In cluster randomized trials, the study units usually are not a simple random sample from some clearly defined
target population. Instead, the target population tends to be hypothetical or ill-defined, and the selection of study
units tends to be systematic, driven by logistical and practical considerations. As a result, the population average
treatment effect (PATE) may be neither well-defined nor easily interpretable. In contrast, the sample average
treatment effect (SATE) is the mean difference in the counterfactual outcomes for the study units. The sample
parameter is easily interpretable and arguably the most relevant when the study units are not sampled from
some specific super-population of interest. Furthermore, in most settings the sample parameter will be estimated
more efficiently than the population parameter. To the best of our knowledge, this is the first paper to propose
using targeted maximum likelihood estimation (TMLE) for estimation and inference of the sample effect in trials
with and without pair-matching. We study the asymptotic and finite sample properties of the TMLE for the
sample effect and provide a conservative variance estimator. Finite sample simulations illustrate the potential
gains in precision and power from selecting the sample effect as the target of inference. This work is motivated
by the Sustainable East Africa Research in Community Health (SEARCH) study, a pair-matched, community
randomized trial to estimate the effect of population-based HIV testing and streamlined ART on the five-year
cumulative HIV incidence (NCT01864603). The proposed methodology will be used in the primary analysis for the
SEARCH trial.
Disciplines
Publication Date
November, 2016
Citation Information
Laura Balzer, M. Petersen, M. van der Laan and the SEARCH Collaboration. "Targeted estimation and inference for the sample average treatment effect in trials with and without pair-matching" Statistics in Medicine (2016) Available at: http://works.bepress.com/laura_balzer/30/