Our Services

Comprehensive Biostatistical and Data Management Services for the Research Community 

For students at the Bloomberg School and researchers inside and outside the Johns Hopkins community, the Center is the go-to resource for biostatistical and data science expertise and support. Across disciplines and throughout the research lifecycle, there’s virtually no data-related issue we can’t help you untangle.  

What Tools Do We Use?

JHBC Statistical Software and programming expertise

Statistical Software

• Stata
• R/RStudio (and associated applications such as Shiny)
• SAS
• SPSS
• BUGS

Programming/Databases

• SQL
• Python
• PHP
• HTML
• Javascript
• REDCap
• .Net

Full Project Lifecycle Support

We offer seamless full-lifecycle research support in biostatistics and data management. Examples include: 

Biostatistics Services 

Study design, protocol review and development

• Sample size justification 
• IRB submission assistance 
• Randomization (from simple to block to complex minimization/covariate-adaptive randomization algorithms) 
• Sampling (e.g., nested, case-cohort, matched case-control studies) 
• Review of data collection instruments 
• General statistical consulting 
• Statistical methods selection and implementation 
• Statistical analysis and interpretation 
• Independent statistical reviews for manuscripts 
• Professional and scientific writing (manuscripts, abstracts, presentations) 
• Independent DSMB report preparation 
• Grant proposal development 
• Study Design and Protocol Development 
• Clinical trials (Phase II, II/III, and III) 
• Basic science experiments 
• Observational studies (cohort and case-based designs) 
• Program evaluation projects 

Data Informatics Services  

REDCap support for database design, setup and modification of the database, user support and custom programming for project requirements as needed. NOTE: Data entry and data monitoring services are not provided. 

Writing programs to perform data management tasks in major statistical packages, such as: 

• Conversion of data structures 
• Data cleaning and preparation for analysis 
• Maintaining data’s quality assurance over the life of the project 
• Protection of PHI fields, as required 
• Creation of additional fields (summaries, new fields based on combinations of other fields) 
• Review of database design and data capture instruments