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A novel calibration framework for survival analysis when a binary covariate is measured at sparse time points.

Authors
  • Nevo, Daniel1
  • Hamada, Tsuyoshi2
  • Ogino, Shuji3, 4, 5
  • Wang, Molin1, 6
  • 1 Departments of Biostatistics and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • 2 Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School Boston, MA, USA.
  • 3 Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
  • 4 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • 5 Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • 6 Channing Division of Network & Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Type
Published Article
Journal
Biostatistics (Oxford, England)
Publication Date
Apr 01, 2020
Volume
21
Issue
2
Identifiers
DOI: 10.1093/biostatistics/kxy063
PMID: 30380012
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The goals in clinical and cohort studies often include evaluation of the association of a time-dependent binary treatment or exposure with a survival outcome. Recently, several impactful studies targeted the association between initiation of aspirin and survival following colorectal cancer (CRC) diagnosis. The value of this exposure is zero at baseline and may change its value to one at some time point. Estimating this association is complicated by having only intermittent measurements on aspirin-taking. Commonly used methods can lead to substantial bias. We present a class of calibration models for the distribution of the time of status change of the binary covariate. Estimates obtained from these models are then incorporated into the proportional hazard partial likelihood in a natural way. We develop non-parametric, semiparametric, and parametric calibration models, and derive asymptotic theory for the methods that we implement in the aspirin and CRC study. We further develop a risk-set calibration approach that is more useful in settings in which the association between the binary covariate and survival is strong. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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