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1Analyses were repeated with inverse probability weighting method using the propensity score.
2Multiple imputation and inverse probability weighting were used to estimate tuberculosis prevalence.
3The results were similar in inverse probability of treatment and censoring weight models.
4We provide SAS code to aid with implementation of inverse probability-of-censoring weighted techniques.
5We applied inverse probability of treatment weighting to adjust for imbalances in treatment assignment.
6Propensity scores and inverse probability treatment weights will be used to adjust for confounding.
7Cox regression was performed, adjusting for group differences with inverse probability of treatment weights.
8A Cox regression model with inverse probability weighting was used to adjust for confounding.
9Propensity score inverse probability of treatment weighting was fitted to balance the measured covariates.
10After applying inverse probability weighting, baseline and severity-of-illness characteristics were well balanced between groups.
11The study used inverse probability of treatment propensity-score weighting.
12Multiple imputation and inverse probability of treatment weighting (IPTW) were used.
13Outcomes were compared between cohorts using inverse probability-weighted analyses.
14To reduce the risk of selection bias, an inverse probability of treatment weighting approach was adopted.
15Propensity score and inverse probability of treatment weighting analysis were performed to control treatment selection bias.
16Sensitivity analyses, using the inverse probability weighting method suggest that our results are unlikely to be biased.
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Inverse probability nas variantes da língua
Estados Unidos da América