Location

Rochester, Minnesota

Contact

Maurer.Matthew@mayo.edu

SUMMARY

The research of Matthew J. Maurer, M.S., is primarily collaborative, contributing data management, study design and statistical analysis expertise to researchers in the Mayo Clinic Cancer Center.

As a member of the Biostatistics Core for Specialized Programs of Research Excellence (SPORE) grants in lymphoma and ovarian cancer and the Lymphoma Epidemiology of Outcomes (LEO) cohort study, Maurer provides statistical support across many different research areas, including molecular epidemiology, clinical trials, basic science with translational, immunological and correlative studies, next-generation sequencing, and general clinical and pathology research. Maurer's personal research involves evaluation of clinical endpoints as well as development of prognostic models and indexes.

Focus areas

  • Lymphoma Molecular Epidemiology Resource (MER) and Lymphoma Epidemiology of Outcomes (LEO) cohort studies. The MER and LEO cohorts are multi-institution epidemiology registries of newly diagnosed patients with lymphoma funded by the University of Iowa and Mayo Clinic SPORE. The MER enrolled more than 6,000 patients with lymphoma from 2002 to 2015, and the LEO cohort has enrolled more than 7,000 patients with lymphoma since 2015. Maurer oversees MER and LEO data content and directs the analysis of studies utilizing the MER and LEO registries.
  • Novel endpoints and prognostic models. Maurer is working to identify patterns of clinical outcomes in patients with lymphoma and ovarian cancer, with the goal of defining optimal endpoints of disease-related outcomes in these populations. He is also developing prognostic models based on these endpoints using nomograms and electronic applications to facilitate use by clinicians, researchers and patients.

Significance to patient care

Maurer's aim is to improve research efficiency by identifying optimal clinical endpoints for evaluation of disease outcome in clinical trials and biological studies of lymphoma. Implementation of risk models into user-friendly nomograms and electronic applications will aid clinicians and patients in risk assessment and prognosis evaluation.

PUBLICATIONS

See my publications
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BIO-00027716

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