Sentence examples for generated propensity from inspiring English sources

The balancing effect of the generated propensity scores is shown in Table 1.

29 30 We generated propensity scores for surgical technique using logistic regression and adjusting for baseline covariates that could influence treatment outcomes, including age, sex, life partner, comorbidity, body mass index, smoking, educational level, number of operated levels, and preoperative Oswestry disability index score.

We observe that different kinds of secondary structure prediction outputs (single-state prediction, single-state prediction with a confidence index, and three-state probability prediction) do not differ greatly in the amount of structural information they yield, so long as the methods formulated in this work to generate propensity distributions are applied appropriately.

These variables were incorporated into a logistic regression model to generate propensity scores [19].

Multiple logistic regressions were used to generate propensity scores, which were the predicted probabilities of AKI requiring dialysis.

21 In this study, a HDPS algorithm was used to generate propensity scores for all patients in the original cohort.

Hence, three types of variables likely to influence the CCP's decision were used to generate propensity scores: time varying variables, non-time varying variables and demand.

The high-dimensional approach to generating propensity scores is an automated data-driven approach to analysing the administrative claims database for variables that appear to be confounders.

Reasons for exclusion were; lack of information about the VAS status or hemoglobin concentration of the children, missing values for the variables needed to generate propensity score and unable to find appropriate matches.

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].