Literature DB >> 30057347

Ghost-time bias from imperfect mortality ascertainment in aging cohorts.

Eric J Jacobs1, Christina C Newton2, Ying Wang2, Peter T Campbell2, W Dana Flanders3, Susan M Gapstur2.   

Abstract

PURPOSE: Many cohort studies in the United States link with the National Death Index to detect deaths. Although linkage with National Death Index is relatively sensitive, some participant deaths will be missed. These participants continue to contribute person-time to the data set after their death, resulting in bias, which we refer to as ghost-time bias. We sought to evaluate the influence of ghost-time bias on mortality relative risk (RR) estimates.
METHODS: Simulations were performed to determine the magnitude of ghost-time bias under a variety of plausible conditions.
RESULTS: Our simulations demonstrate that ghost-time bias can be substantial, particularly among the elderly, where it can reverse the direction of the RR. For example, we conducted a simulation of a cohort of men beginning follow-up at age of 70 years, assuming 5% missed deaths and a true RR of 2.0. In this simulation, observed RRs were 1.89 during the year the cohort was aged 85 years, 1.60 during the year the cohort was aged 90 years, and 0.61 during the year the cohort was aged 95 years. We also provide results from actual cohort data that are consistent with ghost-time bias.
CONCLUSIONS: Ghost-time bias may meaningfully affect mortality RR estimates under conditions that can plausibly occur in aging cohorts.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Aging; Bias; Cohort studies; Epidemiologic methods; Mortality

Mesh:

Year:  2018        PMID: 30057347     DOI: 10.1016/j.annepidem.2018.06.002

Source DB:  PubMed          Journal:  Ann Epidemiol        ISSN: 1047-2797            Impact factor:   3.797


  1 in total

1.  Validation analysis of a composite real-world mortality endpoint for patients with cancer in the United States.

Authors:  Qianyi Zhang; Anala Gossai; Shirley Monroe; Nathan C Nussbaum; Christina M Parrinello
Journal:  Health Serv Res       Date:  2021-05-17       Impact factor: 3.402

  1 in total

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