Exact(2)
Third, propensity scores (for explanation, see Additional file 2) were calculated to estimate the influence of the a priori likelihood of receiving a blood transfusion on the development of a nosocomial infection using a logistic regression model with transfusion as dependent variable.
Because of the linear dependence of the third propensity score on the other two, only the first two propensity scores were included in subsequent analyses of the effect of the confidant social network upon use of residential care.
Similar(58)
First, propensity score methods are best applicable in large data sets.
In the first, propensity for treatment with liposomal bupivacaine was estimated using pretreatment variables and the patients were matched on the propensity score.
First, propensity scores, that is conditional probabilities of experiencing early unemployment, are estimated based on a set of potentially confounding variables.
In addition, a second propensity score was used to adjust for covariates associated with the time to treatment.
First, propensity score matching was used to minimize the observed baseline differences between the insured and uninsured populations.
Second, propensity score matching in non-experimental studies cannot adjust for unobserved covariates, so residual confounding is still possible.
Second, propensity score methods make it easier to determine whether the model has been correctly specified than with regression approaches.
The first Cox proportional hazard model assessed the association between receipt of nafcillin or cefazolin versus vancomycin and mortality controlling for the first propensity score.
The second Cox proportional hazard model assessed the association between switching from vancomycin to nafcillin or cefazolin versus remaining on vancomycin and mortality controlling for the second propensity score.
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