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Propensity scores are used to reduce confounding by equating groups based on these covariates. Suppose that we have a binary treatment indicator Z, a response variable r, and background observed covariates X. The propensity score is defined as the conditional probability of treatment given background variables:
An alternative estimator is the augmented inverse probability weighted estimator (AIPWE) combines both the properties of the regression based estimator and the inverse probability weighted estimator. It is therefore a 'doubly robust' method in that it only requires either the propensity or outcome model to be correctly specified but not both.
Covariate adjustment can be carried out in a variety of ways. Gordon et al. (2018) illustrate many of these methods by means of online advertising data, such as propensity score matching, stratification, regression adjustment, and inverse probability weighted regression adjustment. They find that despite great variation in variables within ...
The propensity score-adjusted result is similar to the univariate result (HR=0.71 [95%CI, 0.52-0.97], p=0.008). After adjustment on different variables (Age, BMI, ASA score, Grade Group, prostate volume, PSA): the risk of salvage treatment is lower in the HIFU arm compared to RP.
This analysis is called 1:1 propensity score matching (PSM). ... Those who habitually skipped breakfast had lower MMSE scores than breakfast eaters. This held true even after adjusting for age ...
[1] [2] [3] Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, [4] but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods. [5]
These may include methods such as post-stratification, raking, or propensity score (estimation) models - used to perform an adjustment of the sample to some known (or estimated) strata sizes. These adjustments can be in addition of design weights , which aims to account for imbalances due to some known sampling design.
The Heckman correction is a statistical technique to correct bias from non-randomly selected samples or otherwise incidentally truncated dependent variables, a pervasive issue in quantitative social sciences when using observational data. [1]