A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study

The propensity score—the probability of exposure to a specific treatment conditional on observed variables—is increasingly being used in observational studies. Creating strata in which subjects are matched on the propensity score allows one to balance measured variables between treated and untreated...

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Bibliographic Details
Published in:Statistics in medicine 2007-02, Vol.26 (4), p.734-753
Main Authors: Austin, Peter C., Grootendorst, Paul, Anderson, Geoffrey M.
Format: Article
Language:English
Subjects:
Aged
Antipsychotic Agents - therapeutic use
balance
Bias
Clinical outcomes
Cohort Studies
Computer Simulation
Confounding Factors (Epidemiology)
Correlation analysis
Data Interpretation, Statistical
Dementia - drug therapy
Drug therapy
Female
Humans
Logistic Models
Male
Models, Statistical
Monte Carlo Method
Monte Carlo simulation
Monte Carlo simulations
Mortality
observational studies
propensity score
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Summary:The propensity score—the probability of exposure to a specific treatment conditional on observed variables—is increasingly being used in observational studies. Creating strata in which subjects are matched on the propensity score allows one to balance measured variables between treated and untreated subjects. There is an ongoing controversy in the literature as to which variables to include in the propensity score model. Some advocate including those variables that predict treatment assignment, while others suggest including all variables potentially related to the outcome, and still others advocate including only variables that are associated with both treatment and outcome. We provide a case study of the association between drug exposure and mortality to show that including a variable that is related to treatment, but not outcome, does not improve balance and reduces the number of matched pairs available for analysis. In order to investigate this issue more comprehensively, we conducted a series of Monte Carlo simulations of the performance of propensity score models that contained variables related to treatment allocation, or variables that were confounders for the treatment–outcome pair, or variables related to outcome or all variables related to either outcome or treatment or neither. We compared the use of these different propensity scores models in matching and stratification in terms of the extent to which they balanced variables. We demonstrated that all propensity scores models balanced measured confounders between treated and untreated subjects in a propensity‐score matched sample. However, including only the true confounders or the variables predictive of the outcome in the propensity score model resulted in a substantially larger number of matched pairs than did using the treatment‐allocation model. Stratifying on the quintiles of any propensity score model resulted in residual imbalance between treated and untreated subjects in the upper and lower quintiles. Greater balance between treated and untreated subjects was obtained after matching on the propensity score than after stratifying on the quintiles of the propensity score. When a confounding variable was omitted from any of the propensity score models, then matching or stratifying on the propensity score resulted in residual imbalance in prognostically important variables between treated and untreated subjects. We considered four propensity score models for estimating treatment effects: t
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.2580