Sentence examples for a propensity model for from inspiring English sources

A propensity model for receiving DrotAA was built using multilevel logistic regression, including patient and unit factors.

Using weights based on a propensity model for statins that did not include the estimated pre-treatment cholesterol we observed a slight protective association (HR = 0.92, 95% CI: 0.54-1.57).

Analysis of outcome based on age >75 years was compared using Cox proportional hazard with a propensity model to adjust for baseline covariates.

Intervention effects, expressed as hazard ratios (HR), were analyzed using Cox regression analysis adjusted for a propensity model to detect subgroup heterogeneity of the effects of rhTM on in-hospital mortality.

The propensity model for using an echinocandin as initial therapy included nine parameters.

The fully adjusted HR using the propensity model for SSSI was 1.3 (95% CI 0.8 to 2.2).

Sensitivity analysis was performed by using 3,164 transfused and non-transfused pairs, matched according the previously validated propensity model for RBC transfusion.

Dichotomous indicators were included in the propensity model for most frequently reported physician types (e.g., cardiovascular specialist and internists) and for specific comorbidities based on presence of diagnosis codes (e.g., neoplasms, pneumonia, hypertension, heart failure, respiratory illness, diabetes, ischemic heart disease, pulmonary vascular disease and stroke).

Given the large number of potential confounders, we used a propensity score model for multivariate analysis.

A sensitivity analysis was performed to investigate the extent of bias that may be present due to this differential dropout using a marginal structural model, re-weighting the propensity model every 6 months for the likelihood of dropping out due to switching.

The response variable for the propensity model was the dichotomous variable for treatment, DrotAA vs. non-DrotAA.