Center for Biostatistics | Icahn School of Medicine

DEPARTMENTS & OFFICES

Department of Population Health Science and Policy

The Division of Biostatistics and Data Science advances statistical and data-driven methodologies through education and collaborative research. We aim to develop innovative statistical approaches to address real-world challenges. By transforming data into actionable insights, we can enhance patient care, advance medical knowledge, and promote public health through rigorous, unbiased, and innovative strategies. The Division is also committed to supporting the professional development of its members. Through graduate programs such as the Master of Science in Biostatistics, seminars, mentoring, and partnerships, we empower students, trainees, and researchers to make meaningful contributions to the health sciences.

Research

We specialize in the design, conduct, and analysis of clinical studies spanning all phases of clinical trials, comparative effectiveness research, and pragmatic trials that evaluate real-world interventions and their impact on patient outcomes. Our research areas include:

Computational Methods and Machine Learning
We develop and apply advanced machine learning algorithms and artificial intelligence approaches to address complex healthcare challenges. Our work includes creating frameworks for medical device safety evaluation, developing predictive models for clinical outcomes, and implementing sophisticated analytical methods that account for temporal effects and learning curves in clinical practice.

High-Dimensional Data Analysis
Our team analyzes complex, high-dimensional datasets including omics data, medical imaging, and digital device information. We employ advanced statistical techniques to extract meaningful patterns from radiomics data, biomarker analyses, and multi-modal clinical datasets that inform patient care decisions.

Health Services and Outcomes Research
We conduct comprehensive analyses of large administrative datasets and claims data to understand healthcare utilization patterns, readmission risks, and population health trends. Our research examines cost-effectiveness of medical procedures, identifies factors that influence patient outcomes, and evaluates healthcare delivery systems to improve quality and efficiency of care.

Clinical Risk Prediction and Monitoring
We develop statistical models for risk stratification and outcome prediction in clinical settings. Our work includes creating tools for perioperative risk assessment, identifying patients at high risk for adverse events, and developing monitoring systems that can detect safety signals and improve patient care protocols.