Michael J. Pencina

Cardiology and Cardiovascular Medicine · 2004–2023

A researcher in the Science corpus: 29 papers; trusts 15 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

Whom Michael J. Pencina trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Ralph B. D’Agostino
  2. James A. Hanley
  3. Nancy R. Cook
  4. William N. Venables
  5. Andrew J. Vickers
  6. Sunil Rao
  7. B. Nam
  8. Peihua Qiu
  9. W. Hoeffding
  10. Peter W. F. Wilson

Who trusts Michael J. Pencina overall

  1. Ewout W. Steyerberg
  2. Karel G. M. Moons
  3. Hajime Uno
  4. J. Ho
  5. Jay P. Singh
  6. Louis Mayaud
  7. Ping Wang
  8. Ralph B. D’Agostino
  9. Chunyu Liu
  10. Ramachandran S. Vasan

Papers from Michael J. Pencina's perspective

Ordered by how strongly the researchers Michael J. Pencina trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. (The Lancet)
  2. What do we mean by validating a prognostic model? (Statistics in Medicine)
  3. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis
  4. Relation between baseline and on-treatment lipid parameters and first acute major coronary events in the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS). (Circulation)
  5. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. (American Journal of Epidemiology)
  6. The Statistical Evaluation of Medical Tests for Classification and Prediction
  7. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III Guidelines. (Journal of the American College of Cardiology)
  8. Estimation of time‐dependent area under the ROC curve for long‐term risk prediction (Statistics in Medicine)
  9. Decision Curve Analysis: A Novel Method for Evaluating Prediction Models
  10. A Class of Statistics with Asymptotically Normal Distribution

Their papers