A comparison of 12 algorithms for matching on the propensity score

Propensity-score matching is increasingly being used to reduce the confounding that can occur in observational studies examining the effects of treatments or interventions on outcomes. We used Monte Carlo simulations to examine the following algorithms for forming matched pairs of treated and untreated subjects: optimal matching, greedy nearest neighbor matching without replacement, and greedy nearest neighbor matching without replacement within specified caliper widths. For each of the latter two algorithms, we examined four different sub-algorithms defined by the order in which treated subjects were selected for matching to an untreated subject: lowest to highest propensity score, highest to lowest…

• HA
  Heart and Stroke Foundation of Canada
• IF
  Institute for Clinical Evaluative Sciences
• OM
  Ontario Ministry of Health and Long-Term Care
• CI
  Canadian Institutes of Health Research

  Awards: 86508, MOP 86508