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

      1 , 2 , 2 , 1 , 3
      Statistical Methods in Medical Research
      SAGE Publications

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          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.

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          Generating survival times to simulate Cox proportional hazards models.

          Simulation studies present an important statistical tool to investigate the performance, properties and adequacy of statistical models in pre-specified situations. One of the most important statistical models in medical research is the proportional hazards model of Cox. In this paper, techniques to generate survival times for simulation studies regarding Cox proportional hazards models are presented. A general formula describing the relation between the hazard and the corresponding survival time of the Cox model is derived, which is useful in simulation studies. It is shown how the exponential, the Weibull and the Gompertz distribution can be applied to generate appropriate survival times for simulation studies. Additionally, the general relation between hazard and survival time can be used to develop own distributions for special situations and to handle flexibly parameterized proportional hazards models. The use of distributions other than the exponential distribution is indispensable to investigate the characteristics of the Cox proportional hazards model, especially in non-standard situations, where the partial likelihood depends on the baseline hazard. A simulation study investigating the effect of measurement errors in the German Uranium Miners Cohort Study is considered to illustrate the proposed simulation techniques and to emphasize the importance of a careful modelling of the baseline hazard in Cox models. Copyright 2005 John Wiley & Sons, Ltd
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            Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse Probability of Censoring Weighted (IPCW) Log-Rank Tests

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              Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers

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                Author and article information

                Journal
                Statistical Methods in Medical Research
                Stat Methods Med Res
                SAGE Publications
                0962-2802
                1477-0334
                March 17 2016
                February 2018
                March 17 2016
                February 2018
                : 27
                : 2
                : 323-335
                Affiliations
                [1 ]Mathematical Institute, Leiden University, Leiden, The Netherlands
                [2 ]Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands
                [3 ]Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands
                Article
                10.1177/0962280216628900
                26988930
                a3a9468c-b3cf-488b-a490-2a3686455c98
                © 2018

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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