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Faculty Member

Aya Mitani

Email Address(es)
Biostatistics Division
Assistant Professor
SGS Status
Associate Member
Appointment Status
Tenure Stream

Education and training

  • Postdoctoral research fellow, Harvard School of Public Health
  • PhD, Biostatistics, Boston University
  • MPH, Biostatistics, Yale University
  • BA, Mathematics, Pitzer College

Research interests

  • Analysis of complex correlated data and methods to handle informative cluster size motivated by dental research
  • Missing data methods motivated by many applications including oncology, nephrology, and anesthesiology
  • Methods for assessing agreement motivated by large-scale mammogram studies


  • CHL5260H: Doctoral Seminar Series in Biostatistics (Fall 2020 & Winter 2021)
  • CHL5222H: Analysis of Correlated Data (Winter 2021)

Recent publications

  • Mitani AA, Haneuse S. Small data challenges of studying rare diseases. Invited Commentary. JAMA Network Open. 2020; 3(3).
  • Mitani AA, Kaye EK, Nelson KP. Marginal analysis of multiple outcomes with informative cluster size. To appear in Biometrics. 2020.
  • Mitani AA, Kaye EK, Nelson KP. Marginal analysis of ordinal clustered longitudinal data with informative cluster size. Biometrics. 2019; 73(3), 938– 949.
  • Nelson KP, Mitani AA, Edwards D. Evaluating the effects of rater and subject factors on measures of association. Biomedical Journal. 2018; 60, 639–656.
  • Mitani AA, Nelson KP. Modeling Agreement between Binary Classifications of Multiple Raters in R and SAS. Journal of Modern Applied Statistical Methods. 2017; 15.
  • Mitani AA, Freer PE, Nelson KP. Summary measures of agreement and association between many raters’ ordinal classifications. Annals of Epidemiology. 2017; 27(10).
  • Nelson KP, Mitani AA, Edwards D. Assessing the influence of rater and subject characteristics on measures of agreement for ordinal ratings. Statistics in Medicine. 2017; 36(20), 3181–3199.
  • Mitani AA, Kurian AW, Das AK, Desai M. Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effects. Statistical Methodology. 2015; 27.

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