Statistical Methods for Health Disparity Research

J.S. Rao
New! Chapman and Hall/CRC Biostatistics Series
Black and white portrait of J. Sunil Rao

J. Sunil Rao's interests are in applied statistics, biostatistics, small area estimation and machine learning. His current research focuses on cancer, health disparities and opioid relapse. He has also developed software packages with various students and collaborators.

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Research Interests:

High throughput cancer genomic data modeling;
Health disparity estimation;
Opioid relapse prediction;
Machine learning;
High dimensional modeling; Bayesian model selection; Mixed model selection
and prediction;
Small area estimation; Bump/mode hunting;
Robust estimation;
Precision medicine;
Modeling of
pharmacogenomic data


Software Sites


A statistical technique for detecting differentially expressing genes from microarray data using Bayesian ANOVA

Fence Methods

A class of strategies for selection of fixed and random factors in linear and generalized linear mixed models
MOst Recent

Publications and Articles


Correcting prevalence estimation for biased sampling with testing errors

L. Zhou, D. Diaz-Pachon, Z. Chen, O. Hoosier, and J.S. Rao
Statistics in Medicine 

Performance of linear mixed models in estimating structural rates of glaucoma progression using varied random effect distributions

S.S. Swaminathan, S.I. Berchuck, J.S. Rao, F.A. Medeiros

A classified mixed effects model reveals that mutated genes CTNNB1, DMD, XIRP2 and PIK3CA are associated with DNA methylation levels in cervical cancer

J. Ramos and J. S. Rao
J. Sunil Rao, Ph.D.

Professor, Division of Biostatistics
School of Public Health
Director of Biostatistics, Masonic Cancer Center
University of Minnesota, Twin Cities

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