Jarrod E Dalton,  PhD

Jarrod E Dalton, PhD

Associate Staff

Lerner Research Institute, 9500 Euclid Avenue, Cleveland, Ohio 44195


My research involves understanding and integrating into clinical practice social, behavioral and environmental factors that affect health. My current research project, which is funded through the National Institute on Aging, involves understanding how the primary drivers of cardiovascular risk might vary across the age spectrum and across the socioeconomic spectrum. Our team is also investigating ways to dynamically adapt cardiovascular risk predictions in response to changes in health status and treatments over time. This work involves the integration of regional electronic medical record data with location-based data from public sources (such as the U.S. Census Bureau, the Environmental Protection Agency, the Centers for Disease Control and Prevention, etc.). We have supported this work by developing open-source software tools for a variety of biomedical research-oriented tasks, primarily through the R statistical programming language.

Lay Summary

My research expertise is in modeling complex systems by combining data from a variety of sources (for instance, electronic medical records, and neighborhood characteristics from agencies such as the U.S. Census and the U.S. Centers for Disease Control and Prevention). In particular, I am currently leading an effort to understand the extent to which various neighborhood and environmental characteristics can be helpful in identifying who is likely to have a heart attack or stroke.


  1. Dalton JE, Perzynski AT, Zidar DA, Rothberg MB, Coulton CJ, Milinovich AT, et al. Accuracy of Cardiovascular Risk Prediction Varies by Neighborhood Socioeconomic Position: A Retrospective Cohort Study. Annals of Internal Medicine 167: 456–64. 2017 [PubMed]
  2. Dalton JE, Zidar DA, Udeh BL, Patel MR, Schold JD, and Dawson NV. Practice Variation among Hospitals in Revascularization Therapy and Its Association with Procedure-Related Mortality. Medical Care 54(6): 623-31. 2016 [PubMed]
  3. Dalton JE, Dawson NV, Sessler DI, Schold JD, Love TE, and Kattan MW.  Empirical Treatment Effectiveness Models for Binary Outcomes. Medical Decision Making 36(1):101-14. 2016 [PubMed]

  4. Dalton JE and Nutter B. HydeNet: Hybrid Bayesian Networks Using R and JAGS. R package version 0.9.0. URL: http://CRAN.R-project.org/package=HydeNet. 2015
  5. Dalton JE. Flexible Recalibration of Binary Clinical Prediction Models. Statistics in Medicine. 32(2): 282-289. 2013 [PubMed]
  6. Dalton JE, Glance LG, Mascha EJ, Ehrlinger J, Chamoun N, Sessler DI. Impact of present-on-admission indicators on risk-adjusted hospital mortality measurement. Anesthesiology 118(6): 1298-1306. 2013 [PubMed] [software link for POARisk model]
  7. Dalton JE, Kurz A, Turan A, Mascha EJ, Sessler DI, Saager L. Development and Validation of a Risk Quantification Index for 30-Day Postoperative Mortality and Morbidity in Noncardiac Surgical Patients. Anesthesiology 114(6):1336-1344. 2011 [PubMed] [software link for RQI model]
  8. Dalton JE, Kattan MW. Recent advances in evaluating the prognostic value of a marker. Scand J Clin Lab Invest Suppl.242:59-62. 2010 [PubMed] 
  9. Dalton JE, Rothberg MB, Dawson NV, Krieger NI, Zidar DA and Perzynski AT. Failure of Traditional Clinical Risk Factors to Adequately Predict Atherosclerotic Cardiovascular Events in Patients Over Age 65. Journal of the American Geriatrics Society (in press) 
  10. Zidar DA, Al-Kindi SG, Liu Y, Krieger NI, Perzynski AT, Osnard M, Nmai C, Anthony DD, Lederman MM, Freeman ML, Bonomo RA, Simon DI and Dalton JE. Association of Lymphopenia With Risk of Mortality Among Adults in the US General Population. JAMA Network Open 2(12):e1916526. 2019 
  11. Krieger NI, Perzynski AT and Dalton JE. Facilitating Reproducible Project Management And Manuscript Development In Team Science: The Projects R Package. PLoS One 14(7), p.e0212390. 2019 [PubMed
  12. Gunzler DD, Perzynski AT, Dawson NV, Kauffman  K, Liu J, and Dalton JE. Risk-period-cohort approach for averting identification problems in longitudinal models. PLoS One 14(7), p.e0219399. 2019 [PubMed
  13. Reimer A and Dalton JE. Predictive accuracy of medical transport information for in-hospital mortality.  Journal of Critical Care 44: 238. 2018 [PubMed]

06/10/2020 |  

Breathing New Life into Lung Transplant Allocation: Policies Require Dynamic Forecast Modeling

With a new grant from the National Heart, Lung, and Blood Institute, Drs. Dalton and Valapour will develop an improved risk modeling approach to help prioritize patients with advanced lung diseases who need a transplant.