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COMPARISON OF DISCRETE DATA METHODS FOR REPEATED MEASURES DATA WITH SMALL SAMPLES

  • Author / Creator
    Zhang, Xuechen
  • Various analytical methods are available to analyze repeated measures data for both continuous and discrete data. In the case of discrete data, most methods are based on the assumption of asymptotic normality, requiring large samples. Naturally, their small sample performance may not match the expectation satisfactorily. Two main methods, the non-linear mixed effects (NLME) model and the generalized estimating equations (GEE) method, are investigated for their small sample performance on repeated binary data. We generated binary data, considering two levels of correlation at rho=0.3 and 0.7, with three cases of repeated measures with T=2, 4, or 6 and sample sizes ranging from 40 to 200. The two analysis methods are applied to each data set in 5000 simulations, and the resulting empirical size and power are compared. We conclude that the GEE performs quite well in small samples with satisfactory empirical size and statistical power and is therefore recommended.

  • Subjects / Keywords
  • Graduation date
    2013-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3XK85190
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Mathematical and Statistical Sciences
  • Specialization
    • Biostatistics
  • Supervisor / co-supervisor and their department(s)
    • Carriere Chough, Keumhee (Department of Mathematical and Statistical Sciences)
  • Examining committee members and their departments
    • Lele, Subhash R. (Department of Mathematical and Statistical Sciences)
    • Dinu, Irina (Department of Public Health Sciences)
    • Carriere Chough, Keumhee (Department of Mathematical and Statistical Sciences)
    • Prasad, N.G.N. (Department of Mathematical and Statistical Sciences)