Sentence examples for propensity weights from inspiring English sources

We used Cox proportional hazard models to compare the overall survival of patients who were receiving palliative chemotherapy with those who were not, with adjustment for propensity weights.

47 If baseline differences are found between the two arms with respect to important confounders (such as age, race, comorbidities), the GEE Wilcoxon rank-sum test will incorporate propensity weights.

The application of propensity weights reduced differences between the two models for all but two patient characteristics (gender, and whether the patient was in a depressive state at the start of the study) and attained standardised differences of less than 0.1, indicating negligible differences [ 31], for five of the twelve characteristics.

44 Current work is being undertaken to develop response propensity weighting adjustments in the MCS4 accelerometer study.

Regression analyses of costs and depression outcomes were conducted, using propensity weighting to control for potential confounders.

Survey responses were merged with de-identified administrative data and this process enabled propensity weighting to adjust for possible non-response bias (described below).

However, adjustment via the propensity weighting method [ 29] showed no significant differences from our original findings (data not shown), suggesting that the respondents adequately represented the entire eligible population.

Further we applied propensity weighting as described by Little and Rubin [ 45] to deal with potential non-response bias; the propensity model included age, sex, government department, employment category, employment condition, and tenure using the human resources administrative database as the reference population.

41 42 In this study, we observed significant differences in the rates of use of chemotherapy by institution, although they were balanced after propensity weighting and therefore did not influence the observed associations between receipt of chemotherapy and patients' end of life medical care and place of death.

Although various weighting schemes have been used with propensity score weights, we choose to perform weighting by the odds to estimate the average treatment effect among the treated.

The propensity score weights are then incorporated as weights into a standard outcome linear regression model with only the treatment as a predictor variable and no covariates [12].