Sentence examples for generated a propensity from inspiring English sources

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].

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).

For the models predicting hospital mortality, and separately for 90-day mortality, we generated a propensity score to balance between the patients' probability of receiving older blood (>14 days in Q2 to Q4) and included patients' blood groups, study sites, and the numbers of transfused units in the scoring.

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.

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.

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.

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.