Hierarchical multiresolution approaches for dense point-level breast cancer treatment data

Shengde Liang, Sudipto Banerjee, Sally Bushhouse, Andrew O. Finley, Brad Carlin

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The analysis of point-level (geostatistical) data has historically been plagued by computational difficulties, owing to the high dimension of the nondiagonal spatial covariance matrices that need to be inverted. This problem is greatly compounded in hierarchical Bayesian settings, since these inversions need to take place at every iteration of the associated Markov chain Monte Carlo (MCMC) algorithm. This paper offers an approach for modeling the spatial correlation at two separate scales. This reduces the computational problem to a collection of lower-dimensional inversions that remain feasible within the MCMC framework. The approach yields full posterior inference for the model parameters of interest, as well as the fitted spatial response surface itself. We illustrate the importance and applicability of our methods using a collection of dense point-referenced breast cancer data collected over the mostly rural northern part of the state of Minnesota. Substantively, we wish to discover whether women who live more than a 60-mile drive from the nearest radiation treatment facility tend to opt for mastectomy over breast conserving surgery (BCS, or "lumpectomy"), which is less disfiguring but requires 6 weeks of follow-up radiation therapy. Our hierarchical multiresolution approach resolves this question while still properly accounting for all sources of spatial association in the data.

Original languageEnglish (US)
Pages (from-to)2650-2668
Number of pages19
JournalComputational Statistics and Data Analysis
Volume52
Issue number5
DOIs
StatePublished - Jan 20 2008

Bibliographical note

Funding Information:
The work of Liang, Banerjee, Bushhouse, and Carlin was supported in part by NIH grant 1-R01-CA95955-01, while the work of Finley was supported by the University of Minnesota Department of Forestry and by NASA Headquarters Earth System Science Fellowship Grant NGT5. The operations of the Minnesota Cancer Surveillance System are supported in part by Cooperative Agreement Number U55/CCU521991 from the Centers for Disease Control and Prevention. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or the CDC.

Keywords

  • Aggregated geographic data
  • Big N problem
  • Breast cancer
  • Conditionally autoregressive (CAR) model
  • Hierarchical modeling
  • Kriging

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