Semiparametric Bayes local additive models for longitudinal data.

TitleSemiparametric Bayes local additive models for longitudinal data.
Publication TypeJournal Article
Year of Publication2015
AuthorsHua, Zhaowei, Hongtu Zhu, and David B. Dunson
JournalStat Biosci
Date Published2015 May 01

In longitudinal data analysis, there is great interest in assessing the impact of predictors on the time-varying trajectory in a response variable. In such settings, an important issue is to account for heterogeneity in the shape of the trajectory among subjects, while allowing the impact of the predictors to vary across subjects. We propose a flexible semiparametric Bayes approach for addressing this issue relying on a local partition process prior, which allows flexible local borrowing of information across subjects. Local hypothesis testing and credible bands are developed for the identification of time windows across which a predictor has a significant impact, while adjusting for multiple comparisons. Posterior computation proceeds via an efficient MCMC algorithm using the exact block Gibbs sampler. The methods are assessed using simulation studies and applied to a yeast cell-cycle gene expression data set.

Alternate JournalStat Biosci
Original PublicationSemiparametric Bayes local additive models for longitudinal data.
PubMed ID26085848
PubMed Central IDPMC4465815
Grant ListR01 MH086633 / MH / NIMH NIH HHS / United States
R01 ES017240 / ES / NIEHS NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R21 AG033387 / AG / NIA NIH HHS / United States