Cora Allen-Savietta
Cora Allen-Savietta is a Statistical Scientist at Berry Consultants. She specializes in designing innovative clinical trials with adaptive sample sizes, complex primary endpoints, and flexible platform structures.
She works in various medical areas, including maternal health, heart failure, and progressive neurodegenerative diseases such as Parkinson’s and Alzheimer’s. She’s passionate about using a Bayesian framework to incorporate previous knowledge into future trial designs. For example, she’s used natural history data to simulate realistic control patients from a disease progression model and designed a phase III trial for a new cancer treatment with dynamic borrowing of the treatment effect across different tumor types. Cora is experienced in guiding statistical discussions and supporting clinical teams through conversations with regulators.
While fluent in statistical theory, she ensures that clarity is not lost in the technical detail and enjoys distilling complex ideas into clear visuals and summaries.
Before joining Berry Consultants, Cora earned a Ph.D. in Statistics from the University of Wisconsin, where she developed statistical methods for phylogenetics, epidemiology, and public health. Prior to graduate school, she worked at Harvard Medical School, studying medication safety and efficacy during pregnancy.
Peer-Reviewed Publications
Marion J, Lorenzi E, Allen-Savietta C, Berry S, Viele K. Predictive Probabilities Made Simple: A Fast and Accurate Method for Clinical Trial Decision Making. Statistics in Medicine. (accepted, in press) (Marion, Lorenzi, & Allen-Savietta co-first authors)
Overbey J, Mentz R, Allen-Savietta C, Navigating Composite Endpoints: Methods and Recommendations for Trial Design and Interpretation. Journal of Cardiac Failure. (accepted, in press)
Heffron AS, Braun KM, Allen-Savietta C, Filut A, Hanewall C, Huttenlocher A, Handelsman J, and Carnes M. Gender Can Influence Student Experiences in MD–PhD Training. Journal of Women’s Health
Fischer MA, Allen-Coleman C, Farrell SF, Schneeweiss S. Stakeholder assessment of comparative effectiveness research needs for Medicaid populations. Journal of Comparative Effectiveness Research
Bateman BT, Huybrechts KF, Maeda A, Desai RJ, Patorno E, Seely EW, Ecker JL, Allen-Coleman C, Mogun H, Hernandez-Diaz S, Fischer MA. Calcium channel blocker exposure in late pregnancy and the risk of neonatal seizures: A cohort study. Obstetrics and Gynecology
Bateman BT, Hernandez-Diaz S, Fischer MA, Seely EW, Ecker JL, Franklin JM, Desai RJ, Allen-Coleman C, Mogun H, Avorn J, Huybrechts KF. Statins and congenital malformations: a cohort study. BMJ
Polinski JM, Kesselheim AS, Frolkis JP, Wescott P, Allen-Coleman C, Fischer MA. A matter of trust: Patient barriers to primary medication adherence. Health Education Research
Conference Presentations
Allen-Savietta, C. ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop September 2025 (invited session accepted)
Allen-Savietta, C. Bridging the Gap between Bayesian and Frequentist Approaches in Clinical Trials for Drug Development. Summer Biometric Seminar, Medical University of Vienna June 2024
Allen-Savietta, C. Forecasting with Confidence: Harnessing Predictive Probabilities in Adaptive Clinical Trial Design. Statisticians in the Pharmaceutical Industry PSI Conference June 2024
Allen-Savietta, C. Forecasting with Confidence: Harnessing Predictive Probabilities in Practice. Society of Clinical Trials May 2024
Allen-Savietta, C. Path to Prevention in Parkinson’s Disease: A Nested Platform Trial to Efficiently Identify Effective Treatments. Women in Statistics and Data Science October 2023
Crawford, A., Quintana, M. Zuckerman, B., Schoenfeld, D. Invited session “Innovation with Hierarchical Composite Endpoints in Complex Trial Design” at ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop September 2023 (served as chair)
Crawford, A. & Allen-Savietta, C. Flexible Bayesian Models for Hierarchical Composite Analyses. International Society for Bayesian Analysis July 2022
Allen-Coleman, C., Gangnon, R.E. Simultaneous Clustering and Ranking of Small Area Health Outcomes Using Nonparametric Empirical Bayes Mixture Models. Contributed presentation at the International Conference on Health Policy Statistics January 2020
Allen-Coleman, C., Ané, Cécile M. Illuminate Evolutionary History with Phylogenetic Networks. Presented at the Joint Statistical Meetings in August 2019
Allen-Coleman, C., Ané, Cécile M. Estimating Evolutionary Rates Efficiently in Phylogenetic Networks. Presented at the Great Lakes Bioinformatics Conference in May 2019
Allen-Coleman, C., Gangnon, R.E. Simultaneous Clustering and Ranking of County-Level Health Outcomes. Contributed presentation at the Society for Epidemiologic Research Meeting in March 2019
Allen-Coleman, C., Gangnon, R.E. Making Ranking Priorities More Explicit. Contributed paper talk at the Joint Statistical Meetings in August 2018
Allen-Coleman, C., Trentham-Dietz, A., McElroy, J.A., Hampton, J.A., Newcomb, P.A., Gangnon, R.E. Geographic Location and Mortality after Breast Cancer Diagnosis. Presented at the Society for Epidemiologic Research Meeting in June 2018