Combining nonexchangeable functional or survival data sources in oncology using generalized mixture commensurate priors

Thomas A Murray, Brian P. Hobbs, Brad Carlin

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Conventional approaches to statistical inference preclude structures that facilitate incorporation of supplemental information acquired from similar circumstances. For example, the analysis of data obtained using perfusion computed tomography to characterize functional imaging biomarkers in cancerous regions of the liver can benefit from partially informative data collected concurrently in noncancerous regions. This paper presents a hierarchical model structure that leverages all available information about a curve, using penalized splines, while accommodating important between-source features. Our proposed methods flexibly borrow strength from the supplemental data to a degree that reflects the commensurability of the supplemental curve with the primary curve. We investigate our method’s properties for nonparametric regression via simulation, and apply it to a set of liver cancer data. We also apply our method for a semiparametric hazard model to data from a clinical trial that compares time to disease progression for three colorectal cancer treatments, while supplementing inference with information from a previous trial that tested the current standard of care.

Original languageEnglish (US)
Pages (from-to)1549-1570
Number of pages22
JournalAnnals of Applied Statistics
Volume9
Issue number3
DOIs
StatePublished - Sep 2015

Keywords

  • Bayesian hierarchical model
  • Clinical trials
  • Colorectal cancer
  • Commensurate prior
  • Computed tomographic imaging
  • Evidence synthesis
  • Mixture priors
  • Penalized splines
  • Proportional hazards
  • Semiparametric methods

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