Literature DB >> 23857273

Persistence clinical studies: can you believe what you see?

Brigitte Cheuvart1, Véronique Bianco, Magalie Caubet, Martine Douha, Laurence Fissette, Nancy François, Anne Sumbul.   

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.

Keywords:  left-censored data; long-term antibody; missing data; persistence; repeated measurement

Mesh:

Substances:

Year:  2013        PMID: 23857273      PMCID: PMC3901829          DOI: 10.4161/hv.24168

Source DB:  PubMed          Journal:  Hum Vaccin Immunother        ISSN: 2164-5515            Impact factor:   3.452


  3 in total

1.  Fabricating data: how substituting values for nondetects can ruin results, and what can be done about it.

Authors:  Dennis R Helsel
Journal:  Chemosphere       Date:  2006-06-05       Impact factor: 7.086

2.  Bias due to left truncation and left censoring in longitudinal studies of developmental and disease processes.

Authors:  Kevin C Cain; Siobán D Harlow; Roderick J Little; Bin Nan; Matheos Yosef; John R Taffe; Michael R Elliott
Journal:  Am J Epidemiol       Date:  2011-03-21       Impact factor: 4.897

3.  Mixed models for longitudinal left-censored repeated measures.

Authors:  Rodolphe Thiébaut; Hélène Jacqmin-Gadda
Journal:  Comput Methods Programs Biomed       Date:  2004-06       Impact factor: 5.428

  3 in total

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