Michael J Pencina, PhD
Adjunct Professor
Boston University School of Public Health
Dept of Biostatistics

PhD, Boston University
MA, University of Warsaw

Dr. Michael Pencina is the Duke Clinical Research Institute Director of Biostatistics. He is Professor of Biostatistics & Bioinformatics at Duke and Adjunct Professor in the Department of Biostatistics at Boston University.

As the DCRI Director of Biostatistics, Dr. Pencina serves as a key institutional leader overseeing the strategic and operational direction of our biostatistics talent and resources at the DCRI. In this role, Dr. Pencina guides a strategic vision to meet the needs of DCRI investigators by developing necessary infrastructure while also partnering with the Biostatistics & Bioinformatics Department to assure that the DCRI’s objectives and services are well understood and supported.

Prior to joining the DCRI in 2013, Dr. Pencina served as an associate professor in the Department of Biostatistics at Boston University and as Director of Statistical Consulting at the Harvard Clinical Research Institute. He brings a wealth of experience in both clinical trials and observational analyses. He has worked on a number of large clinical trials as well as Framingham Heart Study projects. Dr. Pencina is an expert in cardiovascular disease risk prediction model development and assessment of performance. The novel metrics (net reclassification improvement and integrated discrimination improvement) for the assessment of usefulness of new biomarkers and genetic factors in risk prediction proposed by Dr. Pencina have been incorporated into the reporting guidelines. For the past year, Dr. Pencina has also served as the Associate Editor for Statistics in Medicine. He has authored more than 150 manuscripts that have been published in peer-reviewed journals. Dr. Pencina earned his PhD in Mathematics and Statistics at Boston University.
Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.

  1. Pencina KM, D'Agostino RB, Vasan RS, Pencina MJ. Microsimulation model to predict incremental value of biomarkers added to prognostic models. J Am Med Inform Assoc. 2018 Aug 28.View Related Profiles. PMID: 30169699.
  2. Pagidipati NJ, Hellkamp AS, Sharma PP, Wang TY, Fonarow GC, Pencina M. High-sensitivity C-reactive protein elevation in patients with prior myocardial infarction in the United States. Am Heart J. 2018 Jul 27; 204:151-155. PMID: 30121016.
  3. O'Brien EC, Holmes DN, Thomas L, Singer DE, Fonarow GC, Mahaffey KW, Kowey PR, Hylek EM, Pokorney SD, Ansell JE, Pencina MJ, Peterson ED, Piccini JP. Incremental prognostic value of renal function for stroke prediction in atrial fibrillation. Int J Cardiol. 2018 Jul 25.View Related Profiles. PMID: 30144994.
  4. Pencina MJ, Rockhold FW, D'Agostino RB. Deriving Real-World Insights From Real-World Data: Biostatistics to the Rescue. Ann Intern Med. 2018 Sep 18; 169(6):401-402.View Related Profiles. PMID: 30039173.
  5. Thomas LE, O'Brien EC, Piccini JP, D'Agostino RB, Pencina MJ. Application of net reclassification index to non-nested and point-based risk prediction models: a review. Eur Heart J. 2018 Jun 27.View Related Profiles. PMID: 29955849.
  6. Rosenstock J, Perkovic V, Alexander JH, Cooper ME, Marx N, Pencina MJ, Toto RD, Wanner C, Zinman B, Baanstra D, Pfarr E, Mattheus M, Broedl UC, Woerle HJ, George JT, von Eynatten M, McGuire DK. Rationale, design, and baseline characteristics of the CArdiovascular safety and Renal Microvascular outcomE study with LINAgliptin (CARMELINA®): a randomized, double-blind, placebo-controlled clinical trial in patients with type 2 diabetes and high cardio-renal risk. Cardiovasc Diabetol. 2018 Mar 14; 17(1):39. PMID: 29540217.
  7. Leening MJG, Pencina MJ. Absolute vs Additive Net Reclassification Index. JAMA. 2018 02 13; 319(6):616. PMID: 29450519.
  8. Pencina MJ, Chipman J, Steyerberg EW, Braun D, Fine JP, D'Agostino RB. Authors' response to comments. Stat Med. 2017 Dec 10; 36(28):4511-4513.View Related Profiles. PMID: 29156502.
  9. Navar AM, Pencina MJ, Mulder H, Elias P, Peterson ED. Improving patient risk communication: Translating cardiovascular risk into standardized risk percentiles. Am Heart J. 2018 Apr; 198:18-24. PMID: 29653642.
  10. Isakova T, Cai X, Lee J, Xie D, Wang X, Mehta R, Allen NB, Scialla JJ, Pencina MJ, Anderson AH, Talierco J, Chen J, Fischer MJ, Steigerwalt SP, Leonard MB, Hsu CY, de Boer IH, Kusek JW, Feldman HI, Wolf M. Longitudinal FGF23 Trajectories and Mortality in Patients with CKD. J Am Soc Nephrol. 2018 Feb; 29(2):579-590. PMID: 29167351.
Showing 10 of 320 results. Show More

This graph shows the total number of publications by year, by first, middle/unknown, or last author.

Bar chart showing 320 publications over 15 distinct years, with a maximum of 35 publications in 2013

In addition to these self-described keywords below, a list of MeSH based concepts is available here.

cardiovascular disease risk prediction model development
statistics in medicine
data science
machine learning
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801 Massachusetts Ave Crosstown Center
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