RESUMEN
Propensity score method, as an analytical strategy for adjusting multiple covariates, has been widely used in observational comparative effectiveness research. This paper introduces this method covered basic principles, case illustration and software implementation, in order to help readers understand propensity score method, apply it correctly in their researches, improve the efficiency of data utilization, and enhance the level of statistical analysis.
RESUMEN
Confounders are difficult to avoid in studies on observational comparative effectiveness. It is often unclear whether the confounders have been completely eliminated after controlling the measured or unmeasured potential confounding effects or if sensitivity analysis is needed when using the specific statistical methods, under given circumstances. This manuscript summarizes and evaluates the confounding sensitivity analysis methods. Based on different studies, sensitivity analyses need to use different approaches. The traditional sensitivity analysis can be applied for the measured confounders. Currently, the relatively systematic sensitivity analyses for unmeasured confounders would include confounding function, bounding factor and propensity score calibration. Additionally, more investigations are associated with Monte Carlo and Bayesian sensitivity analysis. Reliability of the research conclusion thus may largely be improved when the sensitivity analysis results are consistent with the main analysis.
RESUMEN
Observational comparative effectiveness studies have been widely conducted to provide evidence on additional effectiveness and to support randomized controlled findings in research. Although this type of study becomes more important over time, challenges related to the common biases which stemmed from confounders, are difficult to control. This manuscript summarizes some statistical methods used on adjusting measured confounders that often noticed in research, regarding the observational comparative effectiveness. Useful traditional methods would include stratified analysis, paired analysis, covariate model and multivariable model, etc.. Unconventional adjustment approaches such as propensity score and disease risk score methods may also be used in studies, for matching, stratification and adjustment. A good study design should be able to control confounders. The limitations of all the post hoc statistical adjustment methods should also be fully understood before being appropriately applied in practical events.