Xiaonan Xue
Albert Einstein College of Medicine, NY·Mathematics & Statistics
Cancer research now produces many kinds of complex data and uses a growing range of study designs. Getting sound answers from that data takes dedicated statistical expertise at every stage, from planning a study to publishing it.
- Fields
Mathematics & Statistics
Public Health & Epidemiology
Medicine & Clinical Research
- Topics
biostatistics
bioinformatics
cancer research
- Grants
- 1 known grant, through Jun 2028
What they work on
- Provide biostatistical and bioinformatic collaboration across all phases of cancer studies at the cancer center, from experimental design through data analysis and manuscript preparation.
- Develop methods for novel and pragmatic clinical trials of cancer screening and therapy through a new Subcore of Clinical Trials Methodology, and support scientific review of trial protocols.
- Build machine learning approaches that combine clinical, laboratory and high-dimensional omics data to study cancer causes, prognosis, precision medicine and prevention.
More about the research
This grant supports the Biostatistics Shared Resource of the Montefiore Einstein Comprehensive Cancer Center. Its staff statisticians collaborate with cancer investigators across the center's scientific programs. They provide biostatistical and bioinformatic support for experimental design, study conduct, data analysis and manuscript preparation. They also help build methodologically rigorous grant applications and help develop and review clinical trial protocols. Another goal is to develop new statistical and bioinformatic methods for the analysis challenges raised by new cancer technologies. The resource offers biostatistics training and mentors early-stage cancer investigators. In the next period it is strengthening two areas. A new Subcore of Clinical Trials Methodology focuses on novel and pragmatic trials of cancer screening and therapy. The resource is also building expertise in machine learning methods that combine clinical, laboratory and high-dimensional omics data (large-scale molecular measurements such as genomics). These methods are aimed at questions of cancer causes, prognosis, precision medicine and prevention.
Why it matters
Rigorous statistical design and analysis help cancer studies give reliable results. This resource supports research on cancer causes and prognosis and work to improve cancer prevention, detection and treatment. Better clinical and pragmatic trial methods for screening and therapy could ultimately benefit cancer patients and at-risk populations.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Jennifer Clarke
University of California-San Francisco, CA·Medicine & Clinical Research
Run disease- and discipline-focused site committees that give each proposed cancer clinical trial a first scientific review, judge its feasibility and expected enrollment, and set its priority.
Lawrence Flaherty
Wayne State University, MI·Medicine & Clinical Research
Conduct a two-stage scientific review of cancer clinical trial protocols, first by multidisciplinary disease teams and then by the Protocol Review and Monitoring Committee, judging rationale, design, statistics and feasibility.
Sanjay Shete
University of Texas MD Anderson Cancer Center, TX·Mathematics & Statistics
Provide study design, hypothesis refinement and statistical planning for the Center's clinical and laboratory projects on risk factors for obesity-related cancer.
The school
Albert Einstein College of Medicine: 21 labs on PI Pages, $361M a year on research, 36 research doctorates a year.
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