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Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments

Fan, Huihao
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1407404513

Abstract Details

2014, PhD, University of Cincinnati, Medicine: Biostatistics (Environmental Health).
Real-life count data are frequently characterized by over-dispersion (variance greater than mean) and excess zeros. Various methods exist in literature to combat zero-inflation and over-dispersion in count data. Among them Zero-inflated count models provide a parsimonious yet powerful way to model excess zeros in addition to allowing for over-dispersion. Such models assume that the counts are a mixture of two separate data generation process: one generates only zeros, and the other is a Poisson type data-generating process. Among mostly discussed models are zero-inflated Poisson (ZIP), zero inflated negative binomial (ZINB) and zero-inflated generalized Poisson (ZIGP). However, the performance and application condition of these models are not thoroughly studied. In this work, these common zero-inflation models are reviewed and compared under specified over-dispersion conditions via simulated data and real-life data in terms of statistical power and type I error rate. Performance of each model will be listed side by side to give a clear view of each model’s pros and cons in specific over-dispersion and zero-inflation condition. Further, the ZIGP model is chosen to extend to a more general situation where a random effect is incorporated to account for within-subject correlation and between subject heterogeneity. Likelihood based estimation of treatment effect will be developed for analysis of randomized experiments with random effect. Effect of model misspecification on model’s performance will be investigated in areas such as type I error rate, standard error and empirical statistical power. Case studies will be presented to illustrate the application these models.
Marepalli Rao, Ph.D. (Committee Chair)
Jianfei Guo, Ph.D. (Committee Member)
Rakesh Shukla, Ph.D. (Committee Member)
Paul Succop, Ph.D. (Committee Member)
Changchun Xie, Ph.D. (Committee Member)
124 p.
Biostatistics
over-dispersion; count data; Poisson distribution; zero-inflation

Recommended Citations

Citations

  • Fan, H. (2014). Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1407404513

    APA Style (7th edition)

  • Fan, Huihao. Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments. 2014. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1407404513.

    MLA Style (8th edition)

  • Fan, Huihao. "Test of Treatment Effect with Zero-Inflated Over-Dispersed Count Data from Randomized Single Factor Experiments." Doctoral dissertation, University of Cincinnati, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1407404513

    Chicago Manual of Style (17th edition)

ucin1407404513
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© 2014, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.