title: Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator.
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.
authors.affiliation: 1 Mathematical Institute, Leiden University, Leiden, The Netherlands.
authors.forename: Sjw
authors.initials: S
authors.lastname: Willems
authors.affiliation: 2 Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands.
authors.forename: A
authors.initials: A
authors.lastname: Schat
authors.affiliation: 2 Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands.
authors.forename: M S
authors.initials: MS
authors.lastname: van Noorden
authors.affiliation: 1 Mathematical Institute, Leiden University, Leiden, The Netherlands.|3 Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: M
authors.initials: M
authors.lastname: Fiocco
mesh_terms: D000465:Algorithms; D015982:Bias; D056808:Biostatistics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D015233:Models, Statistical; D011336:Probability; D011379:Prognosis; D016016:Proportional Hazards Models; D018401:Sample Size; D016019:Survival Analysis; D016896:Treatment Outcome
keywords: Survival analysis; dependent censoring; informative censoring; inverse probability censoring weighted estimator; inverse probability weighting