Optimally combining propensity score subclasses.

ABSTRACT: Propensity score methods, such as subclassification, are a common approach to control for confounding when estimating causal effects in non-randomized studies. Propensity score subclassification groups individuals into subclasses based on their propensity score values. Effect estimates are obtained within each subclass and then combined by weighting by the proportion of observations in each subclass. Combining subclass-specific estimates by weighting by the inverse variance is a promising alternative approach; a similar strategy is used in meta-analysis for its efficiency. We use simulation to compare performance of each of the two methods while varying (i) the number of subclasses, (ii) extent of propensity score overlap between the treatment and control groups (i.e., positivity), (iii) i

SUBMITTER: Rudolph KE 

PROVIDER: S-EPMC5096953 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature