Overview
Nikki L. B. Freeman, PhD, joined the Department of Biostatistics and Bioinformatics and the Duke Clinical Research Institute in 2024. Her work focuses on building better, translatable, actionable methods and evidence for health and health care using statistical precision medicine framework. Her technical expertise is in dynamic treatment regimes, (Bayesian) machine learning, Bayesian analysis, topic modeling, clinical trial design including sequential multiple assignment randomized trials (SMARTs), and systematic review and meta-analysis. Her collaborative projects span topics in health services research, global maternal-fetal health, diabetes, and vascular disease.
Current Appointments & Affiliations
Assistant Professor of Biostatistics & Bioinformatics
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2024 - Present
Biostatistics & Bioinformatics, Division of Biostatistics,
Biostatistics & Bioinformatics
Member in the Duke Clinical Research Institute
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2024 - Present
Duke Clinical Research Institute,
Institutes and Centers
Recent Publications
Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case-control study.
Journal Article BMJ Open Diabetes Res Care · February 27, 2024 INTRODUCTION: Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed ... Full text Link to item CiteIndividualized treatment rule characterization via a value function surrogate.
Journal Article Biometrics · January 29, 2024 Precision medicine is a promising framework for generating evidence to improve health and health care. Yet, a gap persists between the ever-growing number of statistical precision medicine strategies for evidence generation and implementation in real-world ... Full text Link to item CiteAssessing risk of bias in the meta-analysis of round 1 of the Health Care Innovation Awards.
Journal Article Syst Rev · January 22, 2024 BACKGROUND: Systematic reviews of observational studies can be affected by biases that lead to under- or over-estimates of true intervention effects. Several tools have been reported in the literature that attempt to characterize potential bias. Our object ... Full text Link to item CiteRecent Grants
Fusing rapid-cycle testing and adaptive trial designs: A scientific pipeline to translate and individualize evidence-based psychosocial and behavioral interventions in routine type 1 diabetes care
ResearchPrincipal Investigator · Awarded by University of North Carolina - Chapel Hill · 2024 - 2027View All Grants
Education, Training & Certifications
University of North Carolina, Chapel Hill ·
2023
Ph.D.