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Thirdly, we used multiple points of censoring for death, which may cause problems of multiple comparisons.
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They may need multiple points of distribution.
With those multiple tiers come multiple points of failure that could be abused, leading to massive privacy breaches.
Then I probably would have used multiple points of view.
Consider multiple points of view.
Science thrives when there are multiple competing points of view.
Since we did not have access to the points of time of censoring, we were not able to correct for this when computing the patient years.
To impute OC values < LOD, we defined an additional function for bootstrap multiple imputation of interval censored variables (Lubin et al. 2004).
We censored event times at a cut-point specified to give a similar proportion of censoring as seen in the original dataset.
For time-to-event outcomes, we used the time point of the host study primary outcome, taking account of censoring if necessary and if the data were available.
We used a Cox proportional-hazards regression model to examine simultaneous effects of multiple covariates on outcomes, censoring patient data at the time of death, or hospital discharge.
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