Bayesian Pathway Analysis for Complex Interactions

James W. Baurley, Anders Kjærsgaard, Michael E. Zwick, Deirdre P. Cronin-Fenton, Lindsay J. Collin, Per Damkier, Stephen Hamilton-Dutoit, Timothy L. Lash, Thomas P. Ahern

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Modern epidemiologic studies permit investigation of the complex pathways that mediate effects of social, behavioral, and molecular factors on health outcomes. Conventional analytical approaches struggle with high-dimensional data, leading to high likelihoods of both false-positive and false-negative inferences. Herein, we describe a novel Bayesian pathway analysis approach, the algorithm for learning pathway structure (ALPS), which addresses key limitations in existing approaches to complex data analysis. ALPS uses prior information about pathways in concert with empirical data to identify and quantify complex interactions within networks of factors that mediate an association between an exposure and an outcome. We illustrate ALPS through application to a complex gene-drug interaction analysis in the Predictors of Breast Cancer Recurrence (ProBe CaRe) Study, a Danish cohort study of premenopausal breast cancer patients (2002-2011), for which conventional analyses severely limit the quality of inference.

Original languageEnglish (US)
Pages (from-to)1610-1622
Number of pages13
JournalAmerican journal of epidemiology
Issue number12
StatePublished - Dec 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Epidemiology


  • Bayesian analysis
  • breast neoplasms
  • pharmacogenetics
  • tamoxifen


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