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Quantitative Health Sciences

Research

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Our Research Programs

Investigators in Quantitative Health Sciences include a diverse group of scientists, ranging in disciplines and specialties, who collaborate with clinical researchers across Cleveland Clinic, outside institutions and industry partners.

Explore all research programs in Quantitative Health Sciences
...
  • Research
  • Sections
    Biostatistics Center for Populations Health Research Clinical Trials Design & Analysis Health Outcomes Research & Clinical Epidemiology Molecular & Translational Data Sciences Research Informatics & Data Engineering Center for Quantitative Metabolic Research
  • Publications
  • Industry Collaborations

Sections

Sections

Biostatistics

Biostatisticians provide statistical support and collaborate with investigators in fields including:

  • Anesthesiology and Critical Care Medicine
  • Basic Science (LRI)
  • Cancer (Taussig)
  • Cardiology
  • Cole Eye
  • Digestive Diseases
  • Education
  • Emergency Medicine
  • Imaging
  • Internal Medicine
  • Neurology
  • Nursing
  • Orthopedics and Rheumatology
  • Pediatrics
  • Pharmacy
  • Respiratory Medicine
  • Surgery
  • Urology and Transplant
  • Women's Health Institute

Biostatisticians support the statistical needs of investigators throughout the research process for clinical trials, experimental/basic research and observational/epidemiologic studies. We provide expertise in grant development, clinical trial design and analysis, and interim monitoring.

During study planning, we advise on design considerations, choice of endpoints and data collection methods. We develop analysis plans using existing and novel methods, create randomization schemes and recommend an appropriate sample size. We assemble and process data, perform appropriate analyses and provide detailed reports. Finally, we help communicate research findings.

Staff Lead: Edward Mascha, PhD


Center for Populations Health Research

The Center for Populations Health (CPHR) offers collaboration for project partnerships, statistical analysis and grant development support for investigators and clinicians working on research projects related to population health. Our mission is to develop, support and promote collaborative research that leads to improved health outcomes and reduced health disparities among diverse populations, and to assist in translating these findings to policy and practice. Our areas of expertise include healthcare disparities and access to care, spatial epidemiology, health economics and decision-analytic modeling, the impact of COVID-19 and large complex survey design.

Staff Lead: Jarrod Dalton, PhD

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Clinical Trials Design & Analysis

Clinical Trials Design & Analysis collaborates with Cleveland Clinic and external investigators, serving as a data coordinating center and team that designs small and large single- and multi-center randomized clinical trials and prospective cohort studies. We oversee data collection and data quality control, and perform analyses for DSMB reports, presentations and manuscripts.

Staff Lead: Wendy King, PhD


Health Outcomes Research & Clinical Epidemiology

Health Outcomes Research & Clinical Epidemiology collaborates with clinical investigators and industry partners on quality-of-life assessment tools, cost effectiveness analyses and epidemiological research. We develop and test state-of-the-art models for use in outcome risk calculators for diseases including cancers, heart disease and diabetes, and help physicians determine the best choices for patient care.

Staff Lead: Daniel Rotroff, PhD


Molecular & Translational Data Sciences

Molecular & Translational Data Sciences works with investigators to design, conduct and interpret analyses of genetic data. Along with our collaborators, we perform a variety of genetic studies, including gene expression, genome-wide association, eQTL, integrated omic and next-generation sequencing studies.

Staff Lead: John Barnard, PhD


Research Informatics & Data Engineering

Research Informatics & Data Engineering includes a diverse team of data scientists, specializing in clinical research studies. Our team focuses on data extraction, registry development and maintenance services for a range of studies, including those related to outcomes, drug and device trials. We have extensive experience in developing and hosting databases for disease and treatment registries, creating tools for data management and supporting API endpoints.

In addition to these capabilities, our team possesses a strong foundation in statistical and machine learning techniques using advanced statistical methods to analyze and derive meaning from complex data sets. We're also highly skilled in natural language processing and parsing free text to extract key information from unstructured data sources.

We have a proven track record of delivering high-quality data solutions that enable researchers to make evidence-based decisions. We offer support in data management, statistical analysis, machine learning, natural language processing and free text parsing.

Staff Leads: Nancy Obuchowski, PhD
Director: Alex Milinovich


Center for Quantitative Metabolic Research

The Center for Quantitative Metabolic Research fosters novel and creative solutions for the challenges of treating and managing metabolic diseases. We strive to revolutionize patient care through the creation of transdisciplinary teams and providing resources to increase research output and drive innovation in patient care.

The center combines the expertise of investigators and clinicians to address the challenges of treating obesity and type 2 diabetes. These diseases impact health in many ways, including:

  • Significant associations with morbidity and mortality
  • Complex manifestations in body systems
  • Links to more than 200 medical complications, conditions and diseases

Historically, the medical field has relied on a 'trial and error' approach to determining the best treatments for patients with obesity or type 2 diabetes. Through the creation of shared resources, including a bariatric surgery biorepository and registries of patients with obesity, type 1 diabetes, and type 2 diabetes, our investigators will help to develop strategies for personalized and targeted treatments for patients.

Staff Leads: Daniel Rotroff, PhD

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Publications

Publications

View a list of publications from Quantitative Health Sciences on PubMed.

Industry Collaborations

Industry Collaborations

Quantitative Health Sciences has extensive experience conducting research for pharmaceutical partners. Cleveland Clinic has invested heavily over the past several decades in its EMR system, which has resulted in one of the largest and most sophisticated EPIC systems in the world. For example, in 2022, the Clinic had nearly 13 million outpatient visits and over 14 million active patients (i.e., those with 2 or more visits/year) worldwide.

Longitudinal data on these types of patients includes not only basic demographics, imaging, laboratory and clinical outcomes data, but also quality-of-life assessments. Using this unique resource, QHS creates longitudinal cohorts for pharma customers defined by disease, treatments, progression rates or ultimate clinical outcomes.

Predictive models and other analytic results from this collaborative research have helped inform:

  • Internal R&D directions
  • The design of future clinical trials
  • The development of improved clinical practices in specific disease areas

For more information, please contact Michael Kattan, PhD, Department Chair, at [email protected].

Industry Publications
  • Addressing Therapeutic Inertia: Development and Implementation of an Electronic Health Record-based Diabetes Intensification Tool
  • Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System
  • The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model
  • Associations of Weight Loss with Obesity-related Comorbidities in a Large Integrated Health System
  • Computable Phenotype of a Crohn's Disease Natural History Model

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