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Danyu Lin

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Danyu Lin
Lin in 2020
Nationality American
Other names Dan-Yu Lin, D. Y. Lin
Alma mater Ph.D. 1989, Biostatistics ,
University of Michigan
Scientific career
Fields Biostatistics
Institutions University 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 advisor Lee-Jen Wei
Website https://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 [ edit ]

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. 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] and Scientific American . [45]

Career [ edit ]

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 2004 and to Professor in 2008. 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 2020 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 Board of Genetic Epidemiology and as a Statistical Reviewer for The Lancet Infectious Diseases .

Honors and Awards [ edit ]

References [ edit ]

  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. ^ "Awards" . Retrieved May 7, 2024 .
  47. ^ "Scientific Legacy Database" . Institute of Mathematical Studies . Retrieved May 7, 2024 .
  48. ^ "ASA Fellows" . American Statistical Association . Retrieved May 7, 2024 .
  49. ^ "2015 G. W. Snedecor Award Winner" . Committee of Presidents of Statistical Societies . Retrieved May 7, 2024 .
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