Institute for Clinical Research and Health Policy Studies

Medical care is delivered one-patient-at-a-time. But the evidence for practicing is derived by aggregating many patients—typically thousands or tens of thousands of patients--into groups. This group-derived evidence would be highly informative for medical practice if all patients were identical. The dissimilarity of individual patients, however, potentially undermines clinical research as a scientific basis for the practice of medicine.

The Predictive Analytics and Comparative Effectiveness (PACE) Center seeks to better understand and address the limitations of using group-derived evidence as the basis for decision making in individual patients. Our approach is based on the close integration of clinical and statistical reasoning. Our goal is to provide clinicians and patients with evidence better tailored to their particular circumstances; we have expertise in clinical medicine, risk modeling, individual patient meta-analysis, and observational comparative effectiveness studies.

David Kent, MD, CM, MSc
Director and Professor of Medicine

June C. Baglione
Senior Research Administrator

Riley Brazil
Research Fellow

Gaurav Gulati, MD
Research Fellow

Mike Hughes
Assistant Professor of Computer Science

David van Klaveren, PhD, MSc
Research Associate

Benjamin Koethe, MPH
Statistician 

Lester Y. Leung, MD, MSc
Director, Comprehensive Stroke Center; Director, Stroke and Young Adults (SAYA) Program

Featured

• Kent DM, Paulus JK, van Klaveren D, et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement. Ann Intern Med. 2020;172:35–45. [Epub ahead of print 12 November 2019]. doi: 10.7326/M18-3667.
  
  
• Kent DM, van Klaveren D, Paulus JK, et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration. Ann Intern Med. 2020;172:W1–W25. [Epub ahead of print 12 November 2019]. doi: 10.7326/M18-3668.
  
  
• Wessler BS, Lundquist CM, Koethe B, Park JG, Brown K, Williamson T, Ajlan M, Natto Z, Lutz JS, Paulus JK, Kent DM. Clinical Prediction Models for Valvular Heart Disease. J Am Heart Assoc. 2019 Oct 15;8(20):e011972. PubMed PMID: 31583938. doi: 10.1161/JAHA.119.011972. Epub 2019 Oct 4.
  
  
• Kent D, Paulus J, Ahmed M, and Whicher D, Editors. 2019. Caring for the Individual Patient: Understanding Heterogeneous Treatment Effects. Washington, DC: National Academy of Medicine.
  
  
• Van Klaveren D, Balan TA, Steyerberg EW, Kent DM. Models with interactions overestimated heterogeneity of treatment effects and were prone to treatment mistargeting. J Clin Epidemiol. 2019 Oct;114:72-83. Pubmed PMID: 31195109. doi: 10.1016/j.jclinepi.2019.05.029.
  
  
• Kent DM, Steyerberg E, van Klaveren D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ. 2018 Dec 10;363:k4245. PubMed PMID: 30530757. doi: 10.1136/bmj.k4245.
  
  
• Van Klaveren D, Steyerberg EW, Serruys PW, Kent DM. The proposed ‘concordance-statistic for benefit’ provided a useful metric when modeling heterogeneous treatment effects. J Clin Epidemiol. 2018 Feb;94:59-68. doi: 10.1016/j.jclinepi.2017.10.021

Individual Investigators

View Dr. Kent’s recent publications on PubMED>

View Mr. Nelson’s recent publications on PubMED>

View Dr. Paulus’ recent publications on PubMED>

View Ms. Ruthazer’s recent publications on PubMED>

View Dr. van Klaveren’s recent publications on PubMED>

View Dr. Wessler’s recent publications on PubMED>

“Heterogeneity of treatment effect and risk-stratified approach for reporting/ implementing clinical trial results.” 16th Global Cardiovascular Clinical Trialists (CVCT) Forum. Washington, DC. December 7, 2019.

“Prediabetes Predictive Model to Personalize Diabetes Risk.” OptumLabs Research and Translation Forum. Boston, MA. November 20, 2019.

“Improving Diabetes Prevention Based on Predicted Benefits of Treatment.” What’s Right For Me? Practical Approaches to Personalized Medicine. PCORI Annual Meeting. Washington, DC. September 18, 2019. 

“Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects in randomized trials.” Society for Clinical Trials Annual Meeting. New Orleans, LA. May 19, 2019.

“Point-Hemoglobin A1c Goals: Is Lower Better? ” Tufts Medical Center Point-Counterpoint Grand Rounds. Boston, MA. October 12, 2018. 

“Evidence and the Individual Patient: Understanding Heterogeneous Treatment Effects for Patient-Centered Care.” National Academy of Medicine. Washington, DC. May 31, 2018.

"Selecting patients for lung cancer screening by personalized risk offers limited long term gains" Tufts Medical Center. Boston, MA. February 1, 2018 

“Personalized Risk Information in Cost Effectiveness Studies (PRICES).” Chapter 14. Health Economics Common Fund Research Symposium. Bethesda, MD. September 25, 2017. 

“Moving Beyond Averages” Patient-Centered Outcomes Research Institute. January 2017

“PACE Symposium: Using Group Data to Treat Individuals” Tufts Medical Center, Boston, MA. June 4, 2015.

“Getting it Right the First Time: Can We Predict Who is Likely to Respond?” The Myth of Average: Why Individual Patient Differences Matter Conference. Omni Shoreham Hotel, Washington DC, Nov 30, 2012.  

“An index to identify stroke-related versus incidental patent foramen ovale in cryptogenic stroke.”  Neurology Podcast. August 13 2013 Issue.

To better understand the extent of Clinical Prediction Model (CPM) development and to help researchers and clinicians, we have created the Tufts PACE CPM Registry, a field synopsis of over 1,000 CPMs that predict clinical outcomes for patients with and at risk for cardiovascular disease.

Predictive Analytics and Comparative Effectiveness (PACE) Center
The Institute for Clinical Research and Health Policy Studies
Tufts Medical Center
800 Washington St., Box 63 
Boston, MA 02111

Jennifer Lutz, MA
Program Coordinator II, Predictive Analytics and Comparative Effectiveness (PACE) Center
Phone: 617-636-7405
Fax: 617-636-0022
https://twitter.com/Tufts_PACE