Statistics in Biosciences

, Volume 5, Issue 2, pp 221–223 | Cite as

Preface to the Issue of Statistics in Biosciences in Honor of Ross Prentice

  • Jianwen Cai
  • Charles KooperbergEmail author
  • Xihong Lin
Ross Prentice is a Full Member in the Division of Public Health Sciences of the Fred Hutchinson Cancer Research Center (FHCRC), of which he was the director for 20 years, and a Professor of Biostatistics at the University of Washington. Ross has made seminal contributions to theory and methods in many areas of biostatistics, and he has been intimately involved in its applications and public health research in general, especially as principal investigator of the Clinical Coordinating Center of the Women’s Health Initiative, a position he also had for about 20 years. This issue is in honor of Ross focusing on the following areas:
  • The Women’s Health Initiative and the population science research agenda

  • Clinical trials

  • Genetic epidemiology

  • Nutritional epidemiology, biomarkers, and measurement error

A companion issue in Lifetime Data Analysis with papers in honor of Ross in the area of survival analysis is planned.

Ross Prentice received his B.Sc. from the University of Waterloo in 1968 and...


Genetic Epidemiology Postmenopausal Hormone Therapy Fred Hutchinson Cancer Research Clinical Coordinate Nutritional Epidemiology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Anderson GL (2013) A statistical perspective on prevention trials: a view from the women’s health initiative. Stat Biosci. doi: 10.1007/s12561-013-9079-8 Google Scholar
  2. 2.
    Breslow NE, Amorim G, Pettinger MB, Rossouw J (2013) Using the whole cohort in the analysis of case-control data. Stat Biosci. doi: 10.1007/s12561-013-9080-2 Google Scholar
  3. 3.
    Cook RJ, Lawless JF (2013) Statistical issued in modeling chronic disease in cohort studies. Stat Biosci. doi: 10.1007/s12561-013-9087-8 Google Scholar
  4. 4.
    Gail MH (2013) Applications of personalized estimates of absolute breast cancer risk. Stat Biosci. doi: 10.1007/s12561-012-9077-2 Google Scholar
  5. 5.
    Gazioglu S, Wei J, Jennings EM, Carroll RJ (2013) A note on penalized regression spline estimation in the secondary analysis of case-control data. Stat Biosci. doi: 10.1007/s12561-013-9094-9 Google Scholar
  6. 6.
    Kalbfleisch JD, Wolfe RA (2013) On monitoring outcomes of medical providers. Stat Biosci. doi: 10.1007/s12561-013-9093-x Google Scholar
  7. 7.
    Lin D (2013) An overview of Ross Prentice’s contributions to statistical science. Stat Biosci. doi: 10.1007/s12561-013-9095-8 Google Scholar
  8. 8.
    Rossouw JE (2013) Menopausal hormone therapy and coronary heart disease: reconciling divergent findings from observational studies and clinical trials. Stat Biosci. doi: 10.1007/s12561-012-9071-8 Google Scholar
  9. 9.
    Toh S, Manson JE (2013) An analytic framework for aligning observational and randomized trial data: application to postmenopausal hormone therapy and coronary heart disease. Stat Biosci. doi: 10.1007/s12561-012-9073-6 Google Scholar
  10. 10.
    Weir BS (2013) Interpreting whole-genome marker data. Stat Biosci. doi: 10.1007/s12561-013-9090-0 Google Scholar

Copyright information

© International Chinese Statistical Association 2013

Authors and Affiliations

  1. 1.Department of BiostatisticsUniversity of North CarolinaChapell HillUSA
  2. 2.Division of Public Health SciencesFred Hutchinson Cancer Research CenterSeattleUSA
  3. 3.Department of BiostatisticsHarvard UniversityBostonUSA

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