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A Bayesian order‐restricted model for hormonal dynamics during menstrual cycles of healthy women
Author(s) -
Roy Anindya,
Danaher Michelle,
Mumford Sunni L.,
Chen Zhen
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4419
Subject(s) - hormone , bayesian probability , multivariate statistics , association (psychology) , menstrual cycle , oxidative stress , bayesian multivariate linear regression , random effects model , medicine , econometrics , physiology , computer science , psychology , linear regression , mathematics , meta analysis , machine learning , artificial intelligence , psychotherapist
We propose a Bayesian framework for analyzing multivariate linear mixed effect models with linear constraints on the fixed effect parameters. The procedure can incorporate both firm and soft restrictions on the parameters and Bayesian model selection for the random effects. The framework is used to analyze data from the BioCycle study. One of the main objectives of the BioCycle study is to investigate the association between markers of oxidative stress and hormone levels during menstrual cycles of healthy women. Contrary to the popular belief that ovarian hormones are negatively associated with level of F 2 ‐isoprostanes, a known marker for oxidative stress, our analysis finds a positive association between ovarian hormone levels and isoprostane levels. The positive association corroborates the findings from a previous analysis of the BioCycle data. Copyright © 2011 John Wiley & Sons, Ltd.