Danyu Lin (biostatistician)

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Danyu Lin
Born1963[
Ph.D. 1989, Biostatistics,
University of Michigan
Scientific career
FieldsBiostatistics
InstitutionsUniversity of North Carolina at Chapel Hill
University of Washington
Thesis Goodness-of-fit tests and robust statistical inference for the Cox proportional hazards model
Doctoral advisorLee-Jen Wei
Websitehttps://dlin.web.unc.edu/

Danyu Lin (

infectious diseases. He is currently the Dennis Gillings Distinguished Professor[1] of Biostatistics at the University of North Carolina at Chapel Hill
.

Research

Lin's early work in survival analysis focused on marginal models for multivariate failure time data, robust inference, and model checking.[2][3][4][5][6]

The statistical methods he developed have been incorporated into major textbooks[7][8] and software packages (SAS, R, Stata, SUDDAN[9]) and used in thousands of scientific studies.[10]

Lin also did groundbreaking research in semiparametric additive risks models and accelerated failure time models.[11][12] Over the last two decades, Lin has made major theoretical and computational advances in nonparametric maximum likelihood estimation of transformation models, random-effects models, and interval-censored data.[13][14]

Lin has made seminal contributions to statistical genetics. His finding that meta-analysis of summary statistics is equivalent to joint analysis of individual-participant data[15][16] has paved the way for geneticists worldwide to discover hundreds of thousands of genetic variants associated with thousands of human diseases and traits through meta-analyses of genome-wide association studies and next-generation sequencing studies. He also pioneered the use of score statistics in genetic association studies,[17][18] which substantially speeds up computation for genome-wide association tests.

Lin made major contributions to the prevention and treatment of COVID-19 by characterizing the time-varying effects of vaccines and prior infections, as well as the benefits of antiviral drugs. His high-profile publications (5 in

The Associated Press,[39] The Wall Street Journal,[40] NBC News,[41] and Science.[42][43]

Career

Lin received his

Fred Hutchinson Cancer Research Center. Lin moved to the University of North Carolina at Chapel Hill at the end of 2020 to become the Dennis Gillings Distinguished Professor of Biostatistics
.

Lin served as an Associate Editor for numerous statistical journals, including

The Lancet Infectious Diseases
.

Honors and Awards

References

  1. ^ "Danyu Lin, PhD". UNC Gillings School of Global Public Health. Retrieved May 7, 2024.
  2. ^ Wei LJ, Lin DY, Weissfeld L (1989). Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. Journal of the American Statistical Association 84: 1065-1073.
  3. ^ Lin DY, Wei LJ (1989). The robust inference for the Cox proportional hazards model. Journal of the American Statistical Association 84: 1074-1078.
  4. ^ Lin DY, Wei LJ, Ying Z (1993). Checking the Cox model with cumulative sums of martingale-based residuals. Biometrika 80: 557-572.
  5. ^ Lin DY (1994). Cox regression analysis of multivariate failure time data: the marginal approach. Statistics in Medicine 13: 2233-2247.
  6. Journal of the Royal Statistical Society - Series B
    62: 711-730.
  7. John Wiley & Sons
    .
  8. ^ Klein JP, Moeschberger ML (2003). Survival Analysis: Techniques for Censored and Truncated Data. New York: Springer.
  9. ^ "SUDDAN: Statistical Software for Weighting, Imputing, and Analyzing Data". Retrieved May 7, 2024.
  10. ^ Google Scholar[1]
  11. ^ Lin DY, Ying Z (1994). Semiparametric analysis of the additive risk model. Biometrika 81: 61-71.
  12. ^ Jin Z, Lin DY, Wei LJ, Ying Z (2023). Rank‐based inference for the accelerated failure time model. Biometrika 90: 341-353.
  13. Journal of the Royal Statistical Society - Series B
    69: 507-564.
  14. ^ Zeng D, Mao L, Lin DY (2016). Maximum likelihood estimation for semiparametric transformation models with interval-censored data. Biometrika 103: 253-271.
  15. ^ Lin DY, Zeng D (2010). Meta-analysis of genome-wide association studies: No efficiency gain in using individual participant data. Genetic Epidemiology 34: 60-66
  16. ^ Lin DY, Zeng D (2010). On the relative efficiency of using summary statistics versus individual-level data in meta-analysis. Biometrika 97: 321-332.
  17. ^ Lin DY (2006). Evaluating statistical significance in two-stage genomewide association studies. American Journal of Human Genetics 78: 505-509.
  18. ^ Lin, DY, Tang ZZ (2011). A general framework for detecting disease associations with rare variants in sequencing studies. American Journal of Human Genetics 89: 354-367.
  19. ^ Lin DY, Baden LR, El Sahly HM, Issink B, Neuzil KM, Corey L, Miller J for the COVE Study Group (2022). Durability of Protection Against Symptomatic COVID-19 Among Participants of the mRNA-1273 SARS-CoV-2 Vaccine Trial. JAMA Network Open 5: e2215984
  20. New England Journal of Medicine
    386: 933-941.
  21. New England Journal of Medicine
    387: 1141-1143.
  22. ^ Lin DY, Gu Y, Xu Y, Wheeler B, Young H, Sunny SK, Moore Z, Zeng D (2022). Association of Primary and Booster Vaccination and Prior Infection With SARS-CoV-2 Infection and Severe COVID-19 Outcomes. JAMA 338: 1415-1426.
  23. ^ Lin DY, Xu Y, Zeng D, Wheeler B, Young H, Moore Z, Sunny SK (2023). Effects of COVID-19 vaccination and previous SARS-CoV-2 infection on omicron infection and severe outcomes in children under 12 years of age in the USA: an observational cohort study.
    The Lancet Infectious Diseases
    23: 1257-1265.
  24. New England Journal of Medicine
    388: 764-766.
  25. New England Journal of Medicine
    388: 1818-1820
  26. ^ Lin DY, Abi Fadel F, Huang S, Milinovich AT, Sacha GL, Bartley P, Duggal A, Wang X (2023). Nirmatrelvir or Molnupiravir Use and Severe Outcomes From Omicron Infections. JAMA Network Open 6: e2335077.
  27. The Lancet Infectious Diseases
    24: 278-280.
  28. New England Journal of Medicine
    388: 2395-2397
  29. FDA
    .
  30. ^ Centers for Disease Control and Prevention (January 13, 2022). "COVID-19 weekly update : Up to date genomics and precision health information on COVID-19".
  31. ^ World Health Organization (October 26, 2022). "COVID-19 weekly epidemiological update, edition 115, 26 October 2022".
  32. ^ Mueller, Benjamin; Lafraniere, Sharon (January 26, 2023). "Covid Vaccines Targeting Omicron Should be Standard, Panel Says". The New York Times.{{cite web}}: CS1 maint: multiple names: authors list (link)
  33. ^ Smith, Dana G. (February 2, 2023). "Who Should Get a Covid Booster Now? New Data Offers Some Clarity". The New York Times.
  34. ^ Krause, Phillip; Gruber, Marion; Offit, Paul (November 29, 2021). "We don't need universal booster shots. We need to reach the unvaccinated". The Washington Post.{{cite news}}: CS1 maint: multiple names: authors list (link)
  35. ^ Wen, Leana (October 20, 2022). "Opinion | The Checkup With Dr. Wen: Should all children get the updated booster?". The Washington Post.
  36. ^ Wen, Leana (February 7, 2023). "Opinion | Should there be an annual coronavirus booster? It depends". The Washington Post.
  37. ^ Wen, Leana (October 5, 2023). "Opinion | The Checkup With Dr. Wen: Paxlovid might be even more important than the new covid shot". The Washington Post.
  38. US News
    .
  39. Associated Press News
    .
  40. WSJ
    .
  41. ^ Ryan, Benjamin (September 24, 2023). "As Covid cases rise, what to know about Paxlovid". NBC News.
  42. ^ Lowe, Derek (February 16, 2023). "There Are Vaccines and There Are Vaccines". Science.
  43. ^ Couzin-Frankel, Jennifer (May 23, 2023). "COVID-19 vaccines may undergo major overhaul this fall". Science.
  44. ^ "Awards". Retrieved May 7, 2024.
  45. ^ "Scientific Legacy Database". Institute of Mathematical Studies. Retrieved May 7, 2024.
  46. ^ "ASA Fellows". American Statistical Association. Retrieved May 7, 2024.
  47. ^ "2015 G. W. Snedecor Award Winner". Committee of Presidents of Statistical Societies. Retrieved May 7, 2024.
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