TY - JOUR

T1 - Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator

AU - Willems, S. J.W.

AU - Schat, A.

AU - van Noorden, M. S.

AU - Fiocco, M.

N1 - Publisher Copyright:
© 2016, © The Author(s) 2016.

PY - 2018/2/1

Y1 - 2018/2/1

N2 - 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.

AB - 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.

KW - dependent censoring

KW - informative censoring

KW - inverse probability censoring weighted estimator

KW - inverse probability weighting

KW - Survival analysis

UR - http://www.scopus.com/inward/record.url?scp=85041956201&partnerID=8YFLogxK

U2 - 10.1177/0962280216628900

DO - 10.1177/0962280216628900

M3 - Article

C2 - 26988930

AN - SCOPUS:85041956201

SN - 0962-2802

VL - 27

SP - 323

EP - 335

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

IS - 2

ER -