Advanced Statistical Models

Proprietary algorithms applied with sophisticated techniques to extract actionable knowledge and insights from data

Examples of advanced statistical and analytical methods that we perform:

selection bias models (propensity score matching, instrumental variables approach)
complex and hierarchical model fitting
clustering, classification, and prediction
survival analysis
longitudinal analysis
multivariate analysis
network and tree analysis
supervised machine learning
integrated high-dimensional omics data analysis

Our experts work in a wide range of complex health care studies, including the following areas:

clinical trial (standard, adaptive, pragmatic) design, analysis, and oversight
observational (case-control, cohort, complex survey, registry, electronic health record, and real world) study design and analysis
laboratory experimental design and analysis
high throughput experiment design and downstream data analysis (microarray, omics sequence data, and others)

Why Choose CDA?

Big Data

Journal Publications

Computer Power