Estimation of the distribution of longitudinal biomarker trajectories prior to disease progression.

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  • Author(s): Huang X;Huang X; Liu L; Liu L; Ning J; Ning J; Li L; Li L; Shen Y; Shen Y
  • Source:
    Statistics in medicine [Stat Med] 2019 May 20; Vol. 38 (11), pp. 2030-2046. Date of Electronic Publication: 2019 Jan 06.
  • Publication Type:
    Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
    • Publication Information:
      Original Publication: Chichester ; New York : Wiley, c1982-
    • Subject Terms:
      Disease Progression*; Biomarkers/*analysis; Humans ; Longitudinal Studies ; Models, Statistical ; Survival Analysis
    • Abstract:
      Most studies characterize longitudinal biomarker trajectories by looking forward at them from a commonly used time origin, such as the initial treatment time. For a better understanding of the relationship between biomarkers and disease progression, we propose to align all subjects by using their disease progression time as the origin and then looking backward at the biomarker distributions prior to that event. We demonstrate that such backward-looking plots are much more informative than forward-looking plots when the research goal is to understand the shape of the trajectory leading up to the event of interest. Such backward-looking plotting is an easy task if disease progression is observed for all the subjects. However, when these events are censored for a significant proportion of subjects in the study cohort, their time origins cannot be identified, and the task of aligning them cannot be performed. We propose a new method to tackle this problem by considering the distributions of longitudinal biomarker data conditional on the failure time. We use landmark analysis models to estimate these distributions. Compared to a naïve method, our new method greatly reduces estimation bias. We apply our method to a study for chronic myeloid leukemia patients whose BCR-ABL transcript expression levels after treatment are good indicators of residual disease. Our proposed method provides a good visualization tool for longitudinal biomarker studies for the early detection of disease.
      (© 2019 John Wiley & Sons, Ltd.)
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    • Grant Information:
      U01 CA152958 United States CA NCI NIH HHS; P30 CA016672 United States CA NCI NIH HHS; P50 CA100632 United States CA NCI NIH HHS; R01 CA193878 United States CA NCI NIH HHS; U54 CA096300 United States CA NCI NIH HHS; R01 HS020263 United States HS AHRQ HHS
    • Contributed Indexing:
      Keywords: biomarker; disease recurrence; landmark analysis; survival analysis
    • Accession Number:
      0 (Biomarkers)
    • Publication Date:
      Date Created: 20190108 Date Completed: 20200826 Latest Revision: 20200826
    • Publication Date:
      20240628
    • Accession Number:
      PMC6501595
    • Accession Number:
      10.1002/sim.8085
    • Accession Number:
      30614014