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Treatment effects from observational studies may be biased as patients are not randomly allocated to a treatment group. Propensity score methods are increasingly being used to address this bias. After propensity score adjustment, the distribution of baseline covariates will be balanced between treated and untreated patients. This paper reviews variable selection, balancing the propensity score, sensitivity analyses and presentation of results for 5 different propensity score methods: covariate adjustment, stratification, inverse probability of treatment weighted (IPTW), stabilized IPTW, and matching. Strengths and limitations of each method are illustrated by estimating the effect of anti-hypertension treatment on survival in advanced stage non-small cell lung cancer patients.

Application of Propensity Score Models in Observational Studies Using VDW Data