More than analysis—statistical collaboration from question to publication
Biostatisticians do much more than analyze data after a study is complete. Our team collaborates with investigators throughout the research process, from developing research questions and designing studies to analyzing data and communicating results.
Explore how we can support your research at each stage.
Strong studies begin with clear, answerable questions. We help investigators translate scientific ideas into testable hypotheses, define meaningful outcomes and comparison groups, and evaluate whether available or proposed data can answer their research questions.
You don't need to have everything figured out before meeting with us. Refining your research question is part of the collaboration.
Early statistical collaboration can help ensure that a study is designed to answer its intended question. We assist with selecting appropriate study designs and endpoints, sample-size and power calculations, randomization, data-collection planning, and statistical analysis plans, including strategies to address potential bias and confounding.
We collaborate with investigators to develop the quantitative components of grant applications and research protocols, including statistical methods, sample-size calculations, analysis plans, and statistical sections. We also help address reviewer comments and plan appropriate biostatistical effort for the proposed research.
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Good analysis begins with good data. We provide guidance on data structure, variable definitions and coding, data dictionaries, REDCap considerations, and data-quality monitoring to help ensure that collected data are ready for analysis.
PREPARING YOUR DATA FOR ANALYSIS
Our biostatisticians have experience with a broad range of statistical methods and cancer research applications. We work with investigators to identify and apply methods appropriate for their scientific questions, study designs, and data.
- Clinical trials
- Laboratory and preclinical studies
- Observational studies
- Meta-analysis and evidence synthesis
- Predictive modeling
- Causal inference
- Confounding adjustment
- Longitudinal and repeated-measures analysis
- Survival and time-to-event analysis
- Missing-data methods
- High-dimensional data analysis
- Omics and high-dimensional molecular data
- Microbiome data
- Imaging research
- Multi-platform and integrative analyses
Not sure which statistical method you need? You don't need to be. Determining the appropriate approach is part of our role.
Statistical analysis does not end when a p-value is produced. We help investigators interpret their findings, develop tables and figures, and communicate results through abstracts, presentations, manuscripts, and responses to reviewers.
Our goal is not simply to determine whether a result is statistically significant, but to help communicate what the data support and what they do not.
Whether you're developing a new study, preparing a grant, or working with existing data, we're here to help determine the right next step.
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