Literature DB >> 29020256

When to Censor?

Catherine R Lesko1, Jessie K Edwards2, Stephen R Cole2, Richard D Moore1,3, Bryan Lau1,3.   

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.

Entities:  

Mesh:

Year:  2018        PMID: 29020256      PMCID: PMC6248498          DOI: 10.1093/aje/kwx281

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  27 in total

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Journal:  Am J Epidemiol       Date:  2009-06-03       Impact factor: 4.897

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  15 in total

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3.  Censoring for Loss to Follow-up in Time-to-event Analyses of Composite Outcomes or in the Presence of Competing Risks.

Authors:  Catherine R Lesko; Jessie K Edwards; Richard D Moore; Bryan Lau
Journal:  Epidemiology       Date:  2019-11       Impact factor: 4.822

4.  Measurement error and misclassification in electronic medical records: methods to mitigate bias.

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Journal:  Curr Epidemiol Rep       Date:  2018-09-10

5.  Gone But Not Lost: Implications for Estimating HIV Care Outcomes When Loss to Clinic Is Not Loss to Care.

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Authors:  Jessica Williams-Nguyen; Stephen E Hawes; Robin M Nance; Sara Lindström; Susan R Heckbert; H Nina Kim; W Chris Mathews; Edward R Cachay; Matt Budoff; Christopher B Hurt; Peter W Hunt; Elvin Geng; Richard D Moore; Michael J Mugavero; Inga Peter; Mari M Kitahata; Michael S Saag; Heidi M Crane; Joseph A Delaney
Journal:  Am J Epidemiol       Date:  2020-06-01       Impact factor: 4.897

7.  Estimating retention in HIV care accounting for clinic transfers using electronic medical records: evidence from a large antiretroviral treatment programme in the Western Cape, South Africa.

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8.  Effect of Metformin and Lifestyle Interventions on Mortality in the Diabetes Prevention Program and Diabetes Prevention Program Outcomes Study.

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10.  Initial Patterns of Prescription Opioid Supply and Risk of Mortality Among Insured Adults in the United States.

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