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Danyu Lin

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Danyu Lin
Lin in 2020
NationalityAmerican
Other namesDan-Yu Lin, D. Y. Lin
Alma materPh.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 (Chinese: 林丹瑜) is a Chinese-American biostatistician known for his contributions to survival analysis, statistical genetics, and infectious diseases. He is currently the Dennis Gillings Distinguished Professor[1] of Biostatistics at the University of North Carolina at Chapel Hill.

Research

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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 conducted 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 enabled geneticists around the world to discover hundreds of thousands of genetic variants associated with thousands of complex 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 important 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 and immunomodulatory agents. His high-profile publications (5 in New England Journal of Medicine, 3 in JAMA journals, and 2 in The Lancet journals)[19][20][21][22][23][24][25][26][27][28] have been viewed over 1 million times; cited by the U.S. Food and Drug Administration,[29][30] Centers for Disease Control and Prevention,[31] and the World Health Organization;[32] and reported by The New York Times,[33][34] The Washington Post,[35][36][37][38] The US News,[39] The Associated Press,[40] The Wall Street Journal,[41] NBC News,[42] Science,[43][44] Scientific American,[45] and Australian Broadcasting Corporation.[46]

Career

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Lin received his Ph.D. in Biostatistics in 1989 from the University of Michigan, where he was supervised by Lee-Jen Wei. After one-year post-doctoral training with Stephen Lagakos at Harvard University, he joined the Biostatistics faculty at the University of Washington, where he was promoted to Associate Professor in 1994 and to Professor in 1998. He also held a joint appointment with the Fred Hutchinson Cancer Research Center. Lin moved to the University of North Carolina at Chapel Hill at the end of 2000 to become the Dennis Gillings Distinguished Professor of Biostatistics.

Lin served as an Associate Editor for numerous statistical journals, including Biometrics (1997-2000), Biometrika (1999-2023), Journal of the American Statistical Association (2012-2023). He also served as a Special Government Employee (Consultant) to the U.S. Food and Drug Administration. He currently serves on the Editorial Boards of Genetic Epidemiology and Vaccines and as a Statistical Reviewer for The Lancet Infectious Diseases.

Honors and awards

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References

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  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. ^ Lin DY, Wei LJ, Yang I, Ying Z (2000). Semiparametric regression for the mean and rate functions of recurrent events. Journal of the Royal Statistical Society - Series B 62: 711-730.
  7. ^ Kalbfleisch JD, Prentice RL (2002). The Statistical Analysis of Failure Time Data. 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. ^ Zeng D, Lin DY (2007). Maximum likelihood estimation in semiparametric regression models with censored data (with discussion). 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. ^ Lin DY, Gu Y, Wheeler B, Young H, Holloway S, Sunny SK, Moore Z, Zeng D (2022). Effectiveness of COVID-19 vaccines over a 9-month period in North Carolina. New England Journal of Medicine 386: 933-941.
  21. ^ Lin DY, Gu Y, Xu Y, Zeng D, Wheeler B, Young H, Sunny SK, Moore Z (2022). Effects of vaccination and previous infection on omicron infections in children. 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. ^ Lin DY, Xu Y, Gu Y, Zeng D, Wheeler B, Young H, Sunny SK, Moore Z (2023). Effectiveness of Bivalent Boosters against Severe Omicron Infection. New England Journal of Medicine 388: 764-766.
  25. ^ Lin DY, Xu Y, Gu Y, Zeng D, Sunny SK, Moore Z (2023). Durability of Bivalent Boosters against Omicron Subvariants. 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. ^ Lin DY, Huang S, Milinovich A, Duggal A, Wang X (2024). Effectiveness of XBB.1.5 vaccines and antiviral drugs against severe outcomes of omicron infection in the USA. The Lancet Infectious Diseases 24: 278-280.
  28. ^ Lin DY, Du Y, Xu Y, Paritala S, Donahue, M, and Maloney P (2024). Durability of XBB.1.5 Vaccines against Omicron Subvariants. New England Journal of Medicine.
  29. ^ Weir, Jerry (January 26, 2023). "Consideration for Potential Changes to COVID-19 Vaccine Strain Composition". FDA.
  30. ^ Weir, Jerry (June 5, 2024). "FDA Considerations and Recommendations for the 2024-2025 COVID-19 Vaccine Formula Composition". FDA.
  31. ^ Centers for Disease Control and Prevention (January 13, 2022). "COVID-19 weekly update : Up to date genomics and precision health information on COVID-19".
  32. ^ World Health Organization (October 26, 2022). "COVID-19 weekly epidemiological update, edition 115, 26 October 2022".
  33. ^ 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)
  34. ^ Smith, Dana G. (February 2, 2023). "Who Should Get a Covid Booster Now? New Data Offers Some Clarity". The New York Times.
  35. ^ 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)
  36. ^ Wen, Leana (October 20, 2022). "Opinion | The Checkup With Dr. Wen: Should all children get the updated booster?". The Washington Post.
  37. ^ Wen, Leana (February 7, 2023). "Opinion | Should there be an annual coronavirus booster? It depends". The Washington Post.
  38. ^ 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.
  39. ^ Foster, Robin (January 27, 2023). "Updated Booster Shots, Not Original COVID Vaccines, Should Be Standard: FDA Panel". US News.
  40. ^ Kelety, Josh (September 15, 2022). "Study finds Pfizer vaccine boosts, not destroys, immunity from past COVID-19 infection". Associated Press News.
  41. ^ Finley, Allysia (January 29, 2023). "Opinion | How Biden Officials Bungled a Better Vaccine". WSJ.
  42. ^ Ryan, Benjamin (September 24, 2023). "As Covid cases rise, what to know about Paxlovid". NBC News.
  43. ^ Lowe, Derek (February 16, 2023). "There Are Vaccines and There Are Vaccines". Science.
  44. ^ Couzin-Frankel, Jennifer (May 23, 2023). "COVID-19 vaccines may undergo major overhaul this fall". Science.
  45. ^ Young, Lauren (June 5, 2024). "New 'FLiRT' COVID Variants Could Be Driving an Uptick in Cases. Here's How to Avoid Them". Scientific American.
  46. ^ Taylor, Tegan and Swan, Norman. (May 31, 2024). "How effective are COVID vaccines against current variants?". Australian Broadcasting Corporation.{{cite web}}: CS1 maint: multiple names: authors list (link)
  47. ^ "Awards". Retrieved May 7, 2024.
  48. ^ "Scientific Legacy Database". Institute of Mathematical Studies. Retrieved May 7, 2024.
  49. ^ "ASA Fellows". American Statistical Association. Retrieved May 7, 2024.
  50. ^ "2015 G. W. Snedecor Award Winner". Committee of Presidents of Statistical Societies. Retrieved May 7, 2024.
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