Biostatistics

Biostatistics

Biostatistics


Biostatistician: Stephen Furmanek

Biostatistics expertise is becoming one of the most sought after qualifications for data science experts. The biostatistics team in the division of infectious diseases has a wide range of experience in the primary field of biostatistics as well as the related fields of epidemiology, geospatial analysis, and data visualization.

Study Design

Any successful study is a product of solid design. We provide high quality consultation and training to guide you through the process of designing your study.

Statistical Planning

Proper statistical planning for your study is critical. We can assist with several key activities in this area including:

  • Generating a verifiable hypothesis.
  • Estimating appropriate sample sizes.
  • Identifying predictor and outcome variables.

Protocol Development

In addition to statistical planning, developing a solid clinical research protocol is extremely important. We can assist with:

  • Understanding and implementing regulatory requirements in protocol development.
  • Defining adverse events for your protocol.
  • Defining the primary and secondary endpoints of the protocol.
  • Determining enrollment and withdrawal requirements.

Statistical Analysis

Appropriate statistical analysis is key to arriving at the appropriate conclusions of the study. We are experts in a number of specialized areas in biostatistics, bioinformatics, and epidemiology including the following: 

  • Sample size estimation
  • Multivariable modeling
  • Machine learning
  • Propensity score analysis
  • Instrumental variables analysis
  • Survival analysis
  • Geospatial analysis

Our team primarily uses the R statistical environment (http://cran.r-project.org) but we are also experienced with all the major statistical software packages including SAS, SPSS, Minitab, MedCalc, Python, and ArcGIS.

We offer training on biostatistical methods annually as well as on a case-by-case basis. This includes basic biostatistical methods such as sample size estimation, assessing correlation and association, evaluation of confounding, medical diagnostic accuracy statistics, as well as using statistical programming languages.