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