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.

Boston University
Evans Center for Interdisciplinary Biomedical Research

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.

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  1. Thomas LE, Turakhia MP, Pencina MJ. Competing Risks, Treatment Switching, and Informative Censoring. JAMA Cardiol. 2021 Aug 01; 6(8):871-873. PMID: 34009256
  2. Thomas LE, Navar AM, Pencina MJ. Extrapolating Survival From Randomized Clinical Trial Data-Possibilities and Caution. JAMA Cardiol. 2021 Jul 28. PMID: 34319362
  3. Singh V, Pencina M, Einstein AJ, Liang JX, Berman DS, Slomka P. Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging. Sci Rep. 2021 Jul 14; 11(1):14490. PMID: 34262098; PMCID: PMC8280147; DOI: 10.1038/s41598-021-93651-5;
  4. Engelhard MM, Navar AM, Pencina MJ. Incremental Benefits of Machine Learning-When Do We Need a Better Mousetrap? JAMA Cardiol. 2021 Jun 01; 6(6):621-623. PMID: 33688913
  5. Jones WS, Mulder H, Wruck LM, Pencina MJ, Kripalani S, Muñoz D, Crenshaw DL, Effron MB, Re RN, Gupta K, Anderson RD, Pepine CJ, Handberg EM, Manning BR, Jain SK, Girotra S, Riley D, DeWalt DA, Whittle J, Goldberg YH, Roger VL, Hess R, Benziger CP, Farrehi P, Zhou L, Ford DE, Haynes K, VanWormer JJ, Knowlton KU, Kraschnewski JL, Polonsky TS, Fintel DJ, Ahmad FS, McClay JC, Campbell JR, Bell DS, Fonarow GC, Bradley SM, Paranjape A, Roe MT, Robertson HR, Curtis LH, Sharlow AG, Berdan LG, Hammill BG, Harris DF, Qualls LG, Marquis-Gravel G, Modrow MF, Marcus GM, Carton TW, Nauman E, Waitman LR, Kho AN, Shenkman EA, McTigue KM, Kaushal R, Masoudi FA, Antman EM, Davidson DR, Edgley K, Merritt JG, Brown LS, Zemon DN, McCormick TE, Alikhaani JD, Gregoire KC, Rothman RL, Harrington RA, Hernandez AF. Comparative Effectiveness of Aspirin Dosing in Cardiovascular Disease. N Engl J Med. 2021 05 27; 384(21):1981-1990. PMID: 33999548
  6. Glówczynska R, Piotrowicz E, Szalewska D, Piotrowicz R, Kowalik I, Pencina MJ, Zareba W, Banach M, Orzechowski P, Pluta S, Irzmanski R, Kalarus Z, Opolski G. Effects of hybrid comprehensive telerehabilitation on cardiopulmonary capacity in heart failure patients depending on diabetes mellitus: subanalysis of the TELEREH-HF randomized clinical trial. Cardiovasc Diabetol. 2021 05 13; 20(1):106. PMID: 33985509; PMCID: PMC8120915; DOI: 10.1186/s12933-021-01292-9;
  7. Allan S, Pencina M, Thanassoulis G. Clinical reasoning and prevention of cardiovascular disease. J Clin Lipidol. 2021 May-Jun; 15(3):394-398. PMID: 33992590
  8. Szalewska D, Glówczynska R, Piotrowicz R, Kowalik I, Pencina MJ, Opolski G, Zareba W, Banach M, Orzechowski P, Pluta S, Irzmanski R, Kalarus Z, Piotrowicz E. An aetiology-based subanalysis of the Telerehabilitation in Heart Failure Patients (TELEREH-HF) trial. ESC Heart Fail. 2021 04; 8(2):1263-1273. PMID: 33527740; PMCID: PMC8006702; DOI: 10.1002/ehf2.13189;
  9. Sriram K, Mulder HS, Frank HR, Santanam TS, Skinner AC, Perrin EM, Armstrong SC, Peterson ED, Pencina MJ, Wong CA. The Dose-Response Relationship Between Physical Activity and Cardiometabolic Health in Adolescents. Am J Prev Med. 2021 01; 60(1):95-103. PMID: 33341183; PMCID: PMC7769140; DOI: 10.1016/j.amepre.2020.06.027;
  10. Pluta S, Piotrowicz E, Piotrowicz R, Lewicka E, Zareba W, Koziel M, Kowalik I, Pencina MJ, Oreziak A, Cacko A, Szalewska D, Glówczynska R, Banach M, Opolski G, Orzechowski P, Irzmanski R, Kalarus Z. Remote Monitoring of Cardiac Implantable Electronic Devices in Patients Undergoing Hybrid Comprehensive Telerehabilitation in Comparison to the Usual Care. Subanalysis from Telerehabilitation in Heart Failure Patients (TELEREH-HF) Randomised Clinical Trial. J Clin Med. 2020 Nov 20; 9(11). PMID: 33233613; PMCID: PMC7699808; DOI: 10.3390/jcm9113729;
Showing 10 of 372 results. Show More

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

Bar chart showing 372 publications over 18 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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