Sentence examples for methods of propensity from inspiring English sources

In the two more recent controlled studies on AKI after intravenous CM administration, sophisticated statistical methods of propensity score adjustment were used to neutralize differences in AKI risk factors between the contrast-enhanced and the noncontrast CT group and thus counteract the effects of selection bias.

Our method of propensity score adjustment was straightforward.

An additional strength is that the method of propensity scores was used to reduce bias in this retrospective study design.

In order to examine the impact of waste picking on MSDs, the study adopted the nearest neighbourhood method of propensity score matching (PSM).

In order to examine the changes in confounder distribution due to IPTW, we calculated standardized biases for the original sample and for the sample following application of IPTW (i.e. the selected method of propensity score application) [ 31- 33].

In time-to-event analyses, applying propensity scores using inverse probability of treatment weights (IPTW) minimizes bias relative to the other methods of applying propensity scores [ 21].

Four methods of using the propensity score have been so far described: stratification [ 1, 2], adjustment [ 1, 2], matching [ 1- 4] and more recently inverse probability of treatment weighting (IPTW) [ 3, 5- 9].

The rationale and methods underlying the use of propensity scores for proposed causal exposure variables have been previously described [ 13, 14].

This article presents only the effects of different matching methods on the balance of propensity score, not on the resulting bias in the estimate of the effect of exposure, which is ultimately what is of interest.

We chose this method from a variety of propensity score matching algorithms because kernel-based matching ensures that all cases in the control group will be used, whereas some other matching algorithms throw away control cases whose propensity scores are not similar to any of the treatment cases [ 17].

The Achilles heel of propensity score methods are unobserved covariates that are unrelated to the observed covariates.