Biostatistics & Bioinformatics Research

Public Health Sciences

DPHS biostatistics faculty focus in a number of areas of methods development. Highlights include: 

  • Posed group photo of some of our biostatistics and bioinformatics facultyClinical trial design and analysis - adaptive designs, randomization schemes, non-inferiority trials
  • Multivariate models involving large number of predictors using random forests for variable selection
  • Threshold identification in multivariate prediction models
  • Analysis of zero-inflated count data from complex surveys
  • Joint modeling of multiple longitudinal outcomes
  • Missing data analysis
  • Mixed models under mixture distributions
  • Latent growth models
  • Zero-inflated and semi-continuous models
  • Analysis of geo-referenced survey data
  • Bayesian multivariate spatio-temporal small area health modeling
  • Spatial censoring in accelerated failure time models
  • Variable selection using leverage and 2 stage estimation for geo-referenced heath data
  • Sparse network Bayesian multivariate exposure models and health outcomes
  • Statistical genetics and Bioinformatics, especially analysis of genome-wide association studies (GWAS) and high throughput sequencing data such as RNA-seq and ChIP-seq
  • Integrative analysis of high throughput genetic and genomic data with biomedical big data
  • High dimensional data analysis
  • Unbalanced cluster designs
  • Multivariate truncated models in data missing due to limits of quantification
  • Other methodological challenges that arise in a variety of collaborative projects across biomedical research disciplines