Linda Valeri, PhD

  • Assistant Professor of Biostatistics

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Overview

Linda Valeri is an expert biostatistician specializing in causal inference, with a focus on biostatistical methodology and statistical learning. Her research encompasses causal mediation analysis, measurement error, missing data, and the integration of data from multiple sources, such as smartphone and wearable devices, life-course cohort studies, and electronic medical records, in diverse populations. Dr. Valeri has developed widely utilized open-access computational tools for causal inference, benefiting scientists across biomedical and social sciences. She collaborates with interdisciplinary teams to advance our understanding of mental health across the life-course, environmental determinants of health, and health disparities, contributing to informed policy-making.She completed a PhD in Biostatistics from Harvard University. Dr. Valeri is a K01 awardee from the National Institute of Mental Health and an R01 awardee from the National Institute of Aging.

Academic Appointments

  • Assistant Professor of Biostatistics

Credentials & Experience

Education & Training

  • BA, 2006 Bocconi University, Milan, Italy
  • MSc, 2008 Bocconi University, Milan, Italy
  • MA, 2010 Harvard University, Cambridge, MA
  • PhD, 2013 Harvard University, Cambridge, MA

Committees, Societies, Councils

Adjunct Assistant Professor, Department of Epidemiology, Harvard T.H. Chan School of Public Health

Honors & Awards

  • 2022 Sanford Bolton Award, MSPH
  • 2021 NIH Mobile Health (mHealth) Training Institute (mHTI) Scholar Award
  • 2020 Columbia Innovation Award, MSPH     
  • 2019 Calderone Young Investigator Award, MSPH
  • 2016 Sarles Young Investigator Award, McLean Hospital
  • 2015 Corneel Young Investigator Award, McLean Hospital
  • 2013 Travel Award Women in Statistics
  • 2013 Italian Scientists and Scholar North America Foundation Award (selected among the top Italian scientists below the age of 40 in North America and invited to present at the Italian Embassy in Washington D.C.)

Research

Research Interests

  • Alzheimer's disease
  • Biostatistical Methods
  • Community Health
  • Disparities/Inequalities in Health
  • Environmental Epidemiology
  • Environmental Health
  • Longitudinal Studies
  • Mental Health
  • Schizophrenia

Selected Publications

  1.   Valeri L, Rahimi H, Liebenthal E, Schutt R, Dixon L, Onnela JP, Baker J (2023). Mobility, Social Activity and Loneliness Monitored Using a Smartphone Application Before and During the Coronavirus Disease 2019 (COVID-19) Epidemic Among Bipolar and Schizophrenia Patients. Schizophrenia, 9(1), 62-62. 
  2.   Fowler, C., Cai, X., Baker, J. T., Onnela, J. P., & Valeri, L. (2023). Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies. Journal of the Royal Statistical Society, Series C.
  3.   Valeri L, Proust-Lima C, Fan W, Chen JT, Jacqmin-Gadda H. (2023). A multistate approach for the study of interventions on an intermediate time-to-event in health disparities research. Statistical Methods in Medical Research. 09622802231163331. 
  4.   Devick KL, Bobb JF, Mazumdar M, Henn BC, Bellinger DC, Christiani DC, ... & Valeri L. (2022). Bayesian kernel machine regression-causal mediation analysis.  Statistics in Medicine, https://doi.org/10.1002/sim.9255. 
  5.   Comment L, Coull BA, Zigler C, & Valeri L. (2022). Bayesian data fusion: Probabilistic sensitivity analysis for unmeasured confounding using informative priors based on secondary data. Biometrics, https://doi.org/10.1111/biom.13436. 
  6.   Shi B, Choirat C, Coull, BA, VanderWeele, TJ, Valeri L. (2021). CMAverse an R package for reproducible causal mediation analysis. Epidemiology, 32(5), e20-e22. 
  7.   Bellavia A, Valeri L. Decomposition of the Total Effect in the Presence of Multiple Mediators and Interactions. Am J Epidemiol. 2018 06 01; 187(6):1311-1318.
  8.  Valeri, L., Lin, X., & VanderWeele, T. J. (2014). Mediation analysis when a continuous mediator is measured with error and the outcome follows a generalized linear model. Statistics in medicine, 33(28), 4875-4890.
  9.  Valeri, L., & Vanderweele, T. J. (2014). The estimation of direct and indirect causal effects in the presence of misclassified binary mediator. Biostatistics, 15(3), 498-512.
  10.   Valeri L, VanderWeele TJ. Mediation analysis allowing for exposure-mediator interaction and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods. 2013; 18(2):137-150.