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      When to Censor?

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          Abstract

          Loss to follow-up is an endemic feature of time-to-event analyses that precludes observation of the event of interest. To our knowledge, in typical cohort studies with encounters occurring at regular or irregular intervals, there is no consensus on how to handle person-time between participants’ last study encounter and the point at which they meet a definition of loss to follow-up. We demonstrate, using simulation and an example, that when the event of interest is captured outside of a study encounter (e.g., in a registry), person-time should be censored when the study-defined criterion for loss to follow-up is met (e.g., 1 year after last encounter), rather than at the last study encounter. Conversely, when the event of interest must be measured within the context of a study encounter (e.g., a biomarker value), person-time should be censored at the last study encounter. An inappropriate censoring scheme has the potential to result in substantial bias that may not be easily corrected.

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          Most cited references22

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          Marginal Structural Models and Causal Inference in Epidemiology

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            Adjusted survival curves with inverse probability weights.

            Kaplan-Meier survival curves and the associated nonparametric log rank test statistic are methods of choice for unadjusted survival analyses, while the semiparametric Cox proportional hazards regression model is used ubiquitously as a method for covariate adjustment. The Cox model extends naturally to include covariates, but there is no generally accepted method to graphically depict adjusted survival curves. The authors describe a method and provide a simple worked example using inverse probability weights (IPW) to create adjusted survival curves. When the weights are non-parametrically estimated, this method is equivalent to direct standardization of the survival curves to the combined study population.
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              Adjusted Kaplan-Meier estimator and log-rank test with inverse probability of treatment weighting for survival data.

              Estimation and group comparison of survival curves are two very common issues in survival analysis. In practice, the Kaplan-Meier estimates of survival functions may be biased due to unbalanced distribution of confounders. Here we develop an adjusted Kaplan-Meier estimator (AKME) to reduce confounding effects using inverse probability of treatment weighting (IPTW). Each observation is weighted by its inverse probability of being in a certain group. The AKME is shown to be a consistent estimate of the survival function, and the variance of the AKME is derived. A weighted log-rank test is proposed for comparing group differences of survival functions. Simulation studies are used to illustrate the performance of AKME and the weighted log-rank test. The method proposed here outperforms the Kaplan-Meier estimate, and it does better than or as well as other estimators based on stratification. The AKME and the weighted log-rank test are applied to two real examples: one is the study of times to reinfection of sexually transmitted diseases, and the other is the primary biliary cirrhosis (PBC) study. 2005 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                Am J Epidemiol
                Am. J. Epidemiol
                aje
                American Journal of Epidemiology
                Oxford University Press
                0002-9262
                1476-6256
                March 2018
                11 August 2017
                01 March 2019
                : 187
                : 3
                : 623-632
                Affiliations
                [1 ]Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
                [2 ]Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
                [3 ]Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland
                Author notes
                Correspondence to Dr. Catherine R. Lesko, Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205 (email: clesko2@ 123456jhu.ed ).
                Article
                PMC6248498 PMC6248498 6248498 kwx281
                10.1093/aje/kwx281
                6248498
                29020256
                d6555d31-cc25-47e2-9eea-f8a8484a7732
                © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
                History
                : 30 November 2016
                : 13 July 2017
                : 14 July 2017
                Page count
                Pages: 10
                Funding
                Funded by: National Institutes of Health (NIH) 10.13039/100000002
                Award ID: U01 DA036935
                Award ID: U01 HL121812
                Award ID: U01 AA020793
                Award ID: P30 AI094189
                Award ID: R01 AI100654
                Categories
                Practice of Epidemiology

                epidemiologic methods,loss to follow-up,selection bias,censoring,survival analysis,bias (epidemiology)

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