Abstract:
:Censored data make survival analysis more complicated because exact event times are not observed. Statistical methodology developed to account for censored observations assumes that patients' withdrawal from a study is independent of the event of interest. However, in practice, some covariates might be associated to both lifetime and censoring mechanism, inducing dependent censoring. In this case, standard survival techniques, like Kaplan-Meier estimator, give biased results. The inverse probability censoring weighted estimator was developed to correct for bias due to dependent censoring. In this article, we explore the use of inverse probability censoring weighting methodology and describe why it is effective in removing the bias. Since implementing this method is highly time consuming and requires programming and mathematical skills, we propose a user friendly algorithm in R. Applications to a toy example and to a medical data set illustrate how the algorithm works. A simulation study was carried out to investigate the performance of the inverse probability censoring weighted estimators in situations where dependent censoring is present in the data. In the simulation process, different sample sizes, strengths of the censoring model, and percentages of censored individuals were chosen. Results show that in each scenario inverse probability censoring weighting reduces the bias induced in the traditional Kaplan-Meier approach where dependent censoring is ignored.
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
Willems S,Schat A,van Noorden MS,Fiocco Mdoi
10.1177/0962280216628900subject
Has Abstractpub_date
2018-02-01 00:00:00pages
323-335issue
2eissn
0962-2802issn
1477-0334pii
0962280216628900journal_volume
27pub_type
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