Due to selection bias, propensity score matching was used to reduce confounding from differences in the patients' baseline characteristics.
Kaplan-Meier estimates Seemingly unrelated regression Correction of sample selection bias Propensity score matching a. Heckman correction b.
In the presence of overt bias, propensity score methods have been proposed as alternatives for estimating treatment effect.
First, to account for technological progress and to avoid selection bias, propensity score analysis was used to compare data on technical parameters.
In order to address this selection bias, propensity score analysis was utilized16.
To control for selection bias, propensity score matching was used to complete a 1 1 match of glargine to exenatide patients.
To achieve the balance in baseline characteristics and to account for the selection bias, propensity score matching was applied.
Those analyses yielded remarkably consistent AHRs (1.465, 1.515, and 1.531 for the lowest to highest propensity score tertiles), indicating that the results shown in Table 2 are not artifacts of the selection bias propensity score adjustment approach.
To obtain the selection bias propensity scores, a multivariable logistic regression was conducted using all appropriate baseline covariates, including veterans' status, to predict exclusion of self-respondents from the analytic sample.
Our (and others' cohort is probably heavily biased by the clinicians propensity to look for EBV in CNS mainly in profoundly immunocompromised patients.
