Semin Liver Dis 2017; 37(03): 275-286
DOI: 10.1055/s-0037-1606213
Review Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Is Propensity Score Analysis a Valid Surrogate of Randomization for the Avoidance of Allocation Bias?

Ferran Torres1, 2, José Ríos1, 2, Joaquín Saez-Peñataro3, Caridad Pontes4
  • 1Medical Statistics Core Facility, IDIBAPS, Hospital Clinic, Barcelona, Spain
  • 2Biostatistics Unit, Faculty of Medicine, Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain
  • 3Clinical Pharmacology Department, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain
  • 4Clinical Pharmacology Unit, Hospital de Sabadell, Institut Universitari Parc Taulí, Universitat Autònoma de Barcelona, Sabadell (Barcelona), Spain
Further Information

Publication History

Publication Date:
28 August 2017 (online)


Randomized clinical trials are the gold standard when experimental designs are feasible. Randomization allows the handling of allocation bias for known and unknown confounders. Specific tools such as blocking, stratification, and dynamic allocation provide additional guarantees to simple randomization. When an experimental design is not feasible, the propensity score (PS) has been shown to produce greater benefit than traditional methods (i.e., restriction, stratification, matching and adjusting). There appears to be a hierarchy in terms of the effectiveness of balancing for PS techniques: matching or weighting above stratification above covariate adjustment (which is discouraged due to its drawbacks). Instrumental variable analysis and its variants might provide added value because they aim to balance for unknown confounders as well, thus mimicking randomization, but at present, are considered more useful for sensitivity rather than primary analyses.