Catherine R Lesko1, Jessie K Edwards2, Richard D Moore1,3, Bryan Lau1. 1. From the Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. 2. Department of Epidemiology, University of North Carolina, Chapel Hill, NC. 3. Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD.
Abstract
BACKGROUND: In time-to-event analyses, there is limited guidance on when persons who are lost to follow-up (LTFU) should be censored. METHODS: We simulated bias in risk estimates for: (1) a composite event of measured (outcome only observable in a patient encounter) and captured events (outcome observable outside a patient encounter); and a (2) measured or (3) captured event in the presence of a competing event of the other type, under three censoring strategies: (i) censor at the last study encounter; (ii) censor when LTFU definition is met; and (iii) a new, hybrid censoring strategy. We demonstrate the real-world impact of this decision by estimating: (1) time to acquired immune deficiency syndrome (AIDS) diagnosis or death, (2) time to initiation of antiretroviral therapy (ART), and (3) time to death before ART initiation among adults engaged in HIV care. RESULTS: For (1) our hybrid censoring strategy was least biased. In our example, 5-year risk of AIDS or death was overestimated using last-encounter censoring (25%) and under-estimated using LTFU-definition censoring (21%), compared with results from our hybrid approach (24%). Last-encounter censoring was least biased for (2). When estimating 5-year risk of ART initiation, LTFU-definition censoring underestimated risk (80% vs. 85% using last-encounter censoring). LTFU-definition censoring was least biased for (3). When estimating 5-year risk of death before ART initiation, last-encounter censoring overestimated risk (5.2% vs. 4.7% using LTFU-definition censoring). CONCLUSIONS: The least biased censoring strategy for time-to-event analyses in the presence of LTFU depends on the event and estimand of interest.
BACKGROUND: In time-to-event analyses, there is limited guidance on when persons who are lost to follow-up (LTFU) should be censored. METHODS: We simulated bias in risk estimates for: (1) a composite event of measured (outcome only observable in a patient encounter) and captured events (outcome observable outside a patient encounter); and a (2) measured or (3) captured event in the presence of a competing event of the other type, under three censoring strategies: (i) censor at the last study encounter; (ii) censor when LTFU definition is met; and (iii) a new, hybrid censoring strategy. We demonstrate the real-world impact of this decision by estimating: (1) time to acquired immune deficiency syndrome (AIDS) diagnosis or death, (2) time to initiation of antiretroviral therapy (ART), and (3) time to death before ART initiation among adults engaged in HIV care. RESULTS: For (1) our hybrid censoring strategy was least biased. In our example, 5-year risk of AIDS or death was overestimated using last-encounter censoring (25%) and under-estimated using LTFU-definition censoring (21%), compared with results from our hybrid approach (24%). Last-encounter censoring was least biased for (2). When estimating 5-year risk of ART initiation, LTFU-definition censoring underestimated risk (80% vs. 85% using last-encounter censoring). LTFU-definition censoring was least biased for (3). When estimating 5-year risk of death before ART initiation, last-encounter censoring overestimated risk (5.2% vs. 4.7% using LTFU-definition censoring). CONCLUSIONS: The least biased censoring strategy for time-to-event analyses in the presence of LTFU depends on the event and estimand of interest.
Authors: John J McNeil; Robyn L Woods; Mark R Nelson; Christopher M Reid; Brenda Kirpach; Rory Wolfe; Elsdon Storey; Raj C Shah; Jessica E Lockery; Andrew M Tonkin; Anne B Newman; Jeff D Williamson; Karen L Margolis; Michael E Ernst; Walter P Abhayaratna; Nigel Stocks; Sharyn M Fitzgerald; Suzanne G Orchard; Ruth E Trevaks; Lawrence J Beilin; Geoffrey A Donnan; Peter Gibbs; Colin I Johnston; Joanne Ryan; Barbara Radziszewska; Richard Grimm; Anne M Murray Journal: N Engl J Med Date: 2018-09-16 Impact factor: 91.245
Authors: Bryan E Shepherd; Meridith Blevins; Lara M E Vaz; Troy D Moon; Aaron M Kipp; Eurico José; Ferreira G Ferreira; Sten H Vermund Journal: Am J Epidemiol Date: 2013-06-19 Impact factor: 4.897