Propensity Score Analysis Statistical Methods And Applications Pdf
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- Guo Fraser Propensity Score Analysis Statistical Methods and Applications
- Propensity score matching
- Propensity Score Analysis: Statistical Methods and Applications
Guo Fraser Propensity Score Analysis Statistical Methods and Applications
The PS is a probability. We can calculate a PS for each subject in an observational study regardless of her actual exposure. Once we have a PS for each subject, we then return to the real world of exposed and unexposed. We can match exposed subjects with unexposed subjects with the same or very similar PS. Thus, the probability of being exposed is the same as the probability of being unexposed. Propensity score analysis PSA arose as a way to achieve exchangeability between exposed and unexposed groups in observational studies without relying on traditional model building.
In the statistical analysis of observational data , propensity score matching PSM is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. PSM attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect obtained from simply comparing outcomes among units that received the treatment versus those that did not. Paul R. Rosenbaum and Donald Rubin introduced the technique in The possibility of bias arises because a difference in the treatment outcome such as the average treatment effect between treated and untreated groups may be caused by a factor that predicts treatment rather than the treatment itself. In randomized experiments , the randomization enables unbiased estimation of treatment effects; for each covariate, randomization implies that treatment-groups will be balanced on average, by the law of large numbers.
Propensity score matching
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Propensity Score Analysis: Statistical Methods and Applications
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Propensity score analysis is a versatile statistical method used mainly in observational studies for improving treatment comparison by adjusting for up to a relatively large number of potentially confounding covariates. Recently, there has been an increased interest in applying this method to nonrandomized medical device clinical studies. In the application of the methodology, some statistical and regulatory issues arise in both study design and analysis of study results, such as the need for pre-specifying clinically relevant covariates to be measured, appropriate patient populations, and the essential elements of statistical analysis, planning sample size in the context of propensity score methodology, handling missing covariates in generating propensity scores, and assessing the success of the propensity score method by evaluating treatment group overlap in terms of the distributions of propensity scores. In this paper, the advantages and limitations of this methodology will be revisited, and the above issues will be discussed and illustrated with examples from a regulatory perspective.
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