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Andrea Bellavia

Lecturer

Harvard University

United States

Research Interests

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About

Andrea Bellavia is an Assistant Professor at Harvard University in the United States. Her research encompasses cardiovascular epidemiology, clinical trial methodologies, and advanced statistical techniques for healthcare data analysis. Recent works include investigations into clinical risk scores, sodium-glucose cotransporter-2 inhibitors for heart failure, and methods to ensure interpretable results in exposome research.

Articles (17)

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2026

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2024

Collaborators (3)

Nicola Orsini

Karolinska Institutet

SWEDEN

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Ashish Verma

Assistant Professor of Medicine

Boston University School of Medicine

UNITED STATES

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Henrik Kjærulf Jensen

VIP, Clinical Professor

Aarhus University

DENMARK

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Andrea Bellavia

Research Interests

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Articles (17)

Effects of Evolocumab on Mortality Outcomes in Patients Without Previous Myocardial Infarction or Stroke: A Prespecified Analysis of the VESALIUS-CV Randomized Clinical Trial.

Missing lactate values in critical care registries: a multiple imputation framework for cardiogenic shock research

Total Event Analysis in Cardiovascular Outcome Trials: Approaches and Interpretation

Wqsreg: a Stata command for weighted quantile sum regression

Competing Risks in Cardiovascular Research: A Practical Perspective

Modern Computational Tools to Support The Implementation and Accessibility of Clinical Risk Scores

Dapagliflozin in Patients Hospitalized for Heart Failure: Primary Results of the DAPA ACT HF-TIMI 68 Randomized Clinical Trial and Meta-Analysis of Sodium-Glucose Cotransporter-2 Inhibitors in Patients Hospitalized for Heart Failure

Complex methods for complex data: key considerations for interpretable and actionable results in exposome research

The Associations of Foot Process Effacement with Kidney Histopathologic Lesions and Disease Progression

Confounders, Effect Modifiers, and Mediators: Dealing With “Third Variables” in Cardiovascular Epidemiology

Estimating and presenting non-linear associations with restricted cubic splines

Clinical Interpretation of Statistical Interaction

Multiple Imputation Approaches for Missing Time-to-Event Outcomes with Informative Censoring: Practical Considerations from a Simulation Study Based on Real Data

Cox Regression Model in Clinical Research: Overview of Key Properties and Interpretation

Linearity in Regression Models: Meaning, Implications, and How to Handle it

Application of machine learning and deep learning approaches for prediction modeling with time-to-event outcomes in clinical epidemiology. Methods comparison and practical considerations for generalizability and interpretability

Heterogeneity of Treatment Effects in Clinical Trials: Overview of Multivariable Approaches and Practical Recommendations

Collaborators (3)

Nicola Orsini

Ashish Verma

Henrik Kjærulf Jensen

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