Sentence examples for generate a propensity from inspiring English sources

Clinical variables including sex, age, T-category, N-category and overall stage were used to generate a propensity score model.

A logistic regression model was used to predict the probability (i.e., generate a propensity score) that a patient would experience CVERP based on observed characteristics.

A total of seven variables that could possibly influence the diagnosis of chromoendoscopy were used to generate a propensity score by logistic regression.

A logistic regression model that adjusted for serial correlation at the hospital level was used to generate a propensity score with the outcome of initiation on CAB vs LF-AMB.

For a given research question and set of patients, we will use logistic regression models (or multi-level logistic regression models for ordinal or multilevel treatments; e.g., levels of treatment dose) to generate a propensity score for each person, using variables that are significant predictors of the intervention of interest.

Finally, a non-parsimonious logistic regression model with procedural timing as the dependent variable was constructed incorporating all baseline clinical and procedural characteristics listed in tables 1 and 2 to generate a propensity score (ie, the predicted probability of procedural timing for each patient), which ranged between 0 and 1 for each patient.

The analysis is performed by first generating a propensity score estimating the probability of treatment assignment then performing a logistic regression adjusted for the propensity score.

We generated a propensity score for each patient, and modelled the RAMP-DM intervention as the dependent variable with the baseline covariates as the independent variables.

We generated a propensity score using multivariable logistic regression with more-intensive RRT dose as the dependent variable, as previously described [ 22, 23].

Propensity score analysis required calculation of the conditional probabilities for the two treatment groups (adjuvant CRT vs. surgery alone) using multivariate regression, generating a propensity score, which was used in multivariate Cox regression model.

We generated a propensity score, based on gene (BRCA1 or BRCA2), tumour size, nodal status, age at diagnosis, year of diagnosis, radiotherapy (yes or no), tamoxifen (yes or no), and chemotherapy (yes or no).