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BOOTSTRAPPING METHODOLOGY IN THE ANALYSIS OF SURVIVAL TIME THROUGH WEIGHTS
Keywords:
censoring, heavy censoring, survival function, Kaplan-Meier estimator, bootstrapping, bias-corrected estimate, standard error, confidence interval.DOI:
https://doi.org/10.17654/0972361722012Abstract
In the real life scenario, all the individuals may not be able to attain the specified event of interest within the specified study time period, which leads to the censoring observations and also sometimes to the problem of heavy censoring. The celebrated Kaplan-Meier estimator (KME) provides over estimates in such heavy censoring data. This problem of overestimation in the survival function was overwhelmed through the improved weighted Kaplan-Meier estimator (IWKME). In this paper, the IWKME is evaluated empirically and it acquires better performance in the case of various levels of censoring as well as for the varying sample size of the bootstrapped datasets. Further, the bootstrapped estimate of the standard error and bias-corrected estimate of the KME and its weighted forms are being compared and its conclusions have been drawn accordingly.
Received: November 2, 2021
Accepted: December 24, 2021
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