Estimation of causal effects of binary treatments in unconfounded studies with one continuous covariate.

ABSTRACT: The estimation of causal effects in nonrandomized studies should comprise two distinct phases: design, with no outcome data available; and analysis of the outcome data according to a specified protocol. Here, we review and compare point and interval estimates of common statistical procedures for estimating causal effects (i.e. matching, subclassification, weighting, and model-based adjustment) with a scalar continuous covariate and a scalar continuous outcome. We show, using an extensive simulation, that some highly advocated methods have poor operating characteristics. In many conditions, matching for the point estimate combined with within-group matching for sampling variance estimation, with or without covariance adjustment, appears to be the most efficient valid method of those evaluat

SUBMITTER: Gutman R 

PROVIDER: S-EPMC4779067 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature