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.
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Authors | Catherine R Lesko, Jessie K Edwards, Richard D Moore, Bryan Lau |
Journal | Epidemiology (Cambridge, Mass.)
(Epidemiology)
Vol. 30
Issue 6
Pg. 817-824
(11 2019)
ISSN: 1531-5487 [Electronic] United States |
PMID | 31393316
(Publication Type: Journal Article, Research Support, N.I.H., Extramural)
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Topics |
- Acquired Immunodeficiency Syndrome
(epidemiology)
- Antiretroviral Therapy, Highly Active
(statistics & numerical data)
- Computer Simulation
- Disease Progression
- Epidemiologic Methods
- HIV Infections
(drug therapy, mortality)
- Humans
- Lost to Follow-Up
- Risk
- Statistics as Topic
- Survival Analysis
- Time-to-Treatment
(statistics & numerical data)
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