Statistical Consultation Statistical Methodologies

A variety of mathematical, statistical, computational, and programming methodologies are employed by the Statistical Consulting & Scientific Programming group at the FNLCR.

Statistical Methods
  • General and Generalized Linear Modeling
  • Analysis of Variance (ANOVA)
  • Linear and Nonlinear Regression Analysis
  • Logistic and Poisson Regression Analysis
  • Mixed and Random Effects Modeling
  • Repeated Measures Analysis
  • Survival and Time-to-Event Analysis
  • Cox Proportional Hazards Regression Analysis
  • Loglinear, Logit, Logistic Analysis
  • Categorical Data Analysis
  • Classification and Discriminant Analysis
  • Machine Learning Algorithms
  • Cluster Analysis and Self Organizing Maps
  • Random Forests
  • Pharmacokinetics, Enzyme Kinetics, and Compartmental Modeling
  • Systems Biology Modeling
  • Image Analysis and Signal Processing
  • Clinical Trials, Endpoint Determination
  • Sample-Size and Power Determination
  • High-Throughput Screening
  • Bioinformatics Analysis and Annotation
  • Data QC, Harmonization and Transformation
  • High-Performance Computing
  • Data Interface and Dashboard Design and Implementation
  • Scientific Programming Support and Consultation in R, Python, SAS