title: Persistence clinical studies: can you believe what you see?
abstract: Long-term immunity, evaluated by the persistence of antibody titers, is important to assess duration of protection induced by vaccination. This paper aims at drawing awareness on the risk of misinterpreting persistence results in absence of adjustment for missing or left-censored data. Using simulations, the paper shows that repeated measurement models are an appropriate alternative to control the bias associated to unadjusted persistence results.
authors.affiliation: GlaxoSmithKline; Wavre, Belgium.
authors.forename: Brigitte
authors.initials: B
authors.lastname: Cheuvart
authors.forename: Véronique
authors.initials: V
authors.lastname: Bianco
authors.forename: Magalie
authors.initials: M
authors.lastname: Caubet
authors.forename: Martine
authors.initials: M
authors.lastname: Douha
authors.forename: Laurence
authors.initials: L
authors.lastname: Fissette
authors.forename: Nancy
authors.initials: N
authors.lastname: François
authors.forename: Anne
authors.initials: A
authors.lastname: Sumbul
mesh_terms: D000906:Antibodies / Q000097:blood*; D004812:Epidemiologic Methods; D006801:Humans; D007158:Immunologic Techniques / Q000379:methods*; D013997:Time Factors; D014612:Vaccines / Q000008:administration & dosage / Q000276:immunology*
chemical_list: D000906:Antibodies; D014612:Vaccines
keywords: left-censored data; long-term antibody; missing data; persistence; repeated measurement