Applying propensity score methods in medical research: pitfalls and prospects.
The authors review experimental and nonexperimental causal inference methods, focusing on assumptions for the validity of instrumental variables and propensity score (PS) methods. They provide guidance in four areas for the analysis and reporting of PS methods in medical research and selectively evaluate mainstream medical journal articles from 2000 to 2005 in the four areas, namely, examination of balance, overlapping support description, use of estimated PS for evaluation of treatment effect, and sensitivity analyses. In spite of the many pitfalls, when appropriately evaluated and applied, PS methods can be powerful tools in assessing average treatment effects in observational studies. Appropriate PS applications can create experimental conditions using observational data when randomized controlled trials are not feasible and, thus, lead researchers to an efficient estimator of the average treatment effect.
Luo
Zhehui
Z
Department of Epidemiology, Michigan State University, East Lansing, MI, USA. zluo@msu.edu
Gardiner
Joseph C
JC
Bradley
Cathy J
CJ
D015982:Bias; D035843:Biomedical Research / Q000379:methods* / Q000592:standards / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D006801:Humans; D018579:Patient Selection; D057216:Propensity Score; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D016896:Treatment Outcome