Petra Buzkova

Summary

Affiliation: University of Washington
Country: USA

Publications

  1. pmc Effects of smoking on the genetic risk of obesity: the population architecture using genomics and epidemiology study
    Megan D Fesinmeyer
    Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109 1024, USA
    BMC Med Genet 14:6. 2013
  2. pmc Investigation of gene-by-sex interactions for lipid traits in diverse populations from the population architecture using genomics and epidemiology study
    Kira C Taylor
    Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
    BMC Genet 14:33. 2013
  3. doi request reprint Measurement error and outcomes defined by exceeding a threshold: biased findings in comparative effectiveness trials
    Petra Buzkova
    Department of Biostatistics, University of Washington, Seattle, WA 98115, USA
    Pharm Stat 11:429-41. 2012
  4. pmc Linear regression in genetic association studies
    Petra Buzkova
    Department of Biostatistics, University of Washington, Seattle, WA, USA
    PLoS ONE 8:e56976. 2013
  5. doi request reprint Semiparametric modeling of repeated measurements under outcome-dependent follow-up
    Petra Buzkova
    Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
    Stat Med 28:987-1003. 2009
  6. pmc Longitudinal data analysis for generalized linear models under participant-driven informative follow-up: an application in maternal health epidemiology
    Petra Buzkova
    Department of Biostatistics, School of Public Health, University of Washington, 6200 NE 74th Street, Seattle, WA 98115, USA
    Am J Epidemiol 171:189-97. 2010
  7. pmc Permutation and parametric bootstrap tests for gene-gene and gene-environment interactions
    Petra Buzkova
    Department of Biostatistics, University of Washington, Seattle, USA
    Ann Hum Genet 75:36-45. 2011

Collaborators

  • Elizabeth R Brown
  • Kari E North
  • Dana C Crawford
  • Brian E Henderson
  • Nora Franceschini
  • Lynne R Wilkens
  • Megan D Fesinmeyer
  • Kira C Taylor
  • Marylyn D Ritchie
  • Christopher Haiman
  • Ulrike Peters
  • Misa Graff
  • Rebecca R Rohde
  • Unhee Lim
  • Steven Buyske
  • Myriam Fornage
  • Myron D Gross
  • Ralph V Shohet
  • Loic Le Marchand
  • Iona Cheng
  • Shelley A Cole
  • Nathalie Schnetz-Boutaud
  • Christopher A Haiman
  • Charles B Eaton
  • Lewis H Kuller
  • José Luis Ambite
  • JoAnn E Manson
  • Christopher S Carlson
  • David J Duggan
  • Lucia Hindorff
  • Garnet Anderson
  • Chris S Carlson
  • Shelley Ann Love
  • P Miguel Quibrera
  • Lucia A Hindorff
  • James Pankow
  • Jay H Fowke
  • Barbara Cochran
  • Cara L Carty
  • Kristin Brown-Gentry
  • Jeff Haessler
  • Charles Kooperberg
  • Fred R Schumacher
  • Melissa Allen
  • Ching Ping Hong
  • Lawrence N Kolonel
  • Petra Bůžková
  • Loïc Le Marchand
  • Karen C Johnson
  • Robert Goodloe
  • Ross L Prentice
  • Ping Mayo
  • Tara C Matise
  • Logan Dumitrescu
  • Jeffrey Haessler
  • Kristine R Monroe

Detail Information

Publications7

  1. pmc Effects of smoking on the genetic risk of obesity: the population architecture using genomics and epidemiology study
    Megan D Fesinmeyer
    Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109 1024, USA
    BMC Med Genet 14:6. 2013
    ..Although smoking behavior is known to affect body mass index (BMI), the potential for smoking to influence genetic associations with BMI is largely unexplored...
  2. pmc Investigation of gene-by-sex interactions for lipid traits in diverse populations from the population architecture using genomics and epidemiology study
    Kira C Taylor
    Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
    BMC Genet 14:33. 2013
    ..Here, we investigated whether GWAS-identified SNPs for lipid traits exhibited heterogeneity by sex in the Population Architecture using Genomics and Epidemiology (PAGE) study...
  3. doi request reprint Measurement error and outcomes defined by exceeding a threshold: biased findings in comparative effectiveness trials
    Petra Buzkova
    Department of Biostatistics, University of Washington, Seattle, WA 98115, USA
    Pharm Stat 11:429-41. 2012
    ..Using simulations and theoretical formulas, we systematically describe the bias of prevalence difference and prevalence ratio when comparing arms and its effect on trial conclusions...
  4. pmc Linear regression in genetic association studies
    Petra Buzkova
    Department of Biostatistics, University of Washington, Seattle, WA, USA
    PLoS ONE 8:e56976. 2013
    ..We conclude that it is a combination of heteroscedasticity, minor allele frequency, sample size, and to a much lesser extent the error distribution, that matter for proper statistical inference...
  5. doi request reprint Semiparametric modeling of repeated measurements under outcome-dependent follow-up
    Petra Buzkova
    Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
    Stat Med 28:987-1003. 2009
    ..We illustrate our approach using data from a randomized health services research study with noncompliance to scheduled visits...
  6. pmc Longitudinal data analysis for generalized linear models under participant-driven informative follow-up: an application in maternal health epidemiology
    Petra Buzkova
    Department of Biostatistics, School of Public Health, University of Washington, 6200 NE 74th Street, Seattle, WA 98115, USA
    Am J Epidemiol 171:189-97. 2010
    ..8%). The estimate obtained using the IIRR-weighted GEE approach was compatible with estimates derived using scheduled visits only. These results highlight the importance of properly accounting for informative follow-up in these studies...
  7. pmc Permutation and parametric bootstrap tests for gene-gene and gene-environment interactions
    Petra Buzkova
    Department of Biostatistics, University of Washington, Seattle, USA
    Ann Hum Genet 75:36-45. 2011
    ..We consider interactions of an exposure with single and multiple polymorphisms. Finally, we address when permutation tests of interaction will be approximately valid in large samples for specific test statistics...