Small Sample Methods for the Analysis of Clustered Binary Data

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ProQuest, 2008 - 213 pages
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There are several solutions for analysis of clustered binary data. However, the two most common tools in use today, generalized estimating equations and random effects or mixed models, rely heavily on asymptotic theory. However, in many situations, such as small or sparse samples, asymptotic assumptions may not be met. For this reason we explore the utility of the quadratic exponential model and conditional analysis to estimate the effect size of a trend parameter in small sample and sparse data settings. Further we explore the computational efficiency of two methods for conducting conditional analysis, the network algorithm and Markov chain Monte Carlo. Our findings indicate that conditional estimates do indeed outperform their unconditional maximum likelihood counterparts. The network algorithm remains the fastest tool for generating the required conditional distribution. However, for large samples, the Markov chain Monte Carlo approach accurately estimates the conditional distribution and is more efficient than the network algorithm.
  

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Contents

ESTIMATION APPROACHES FOR SMALL OR SPARSE SAMPLES
25
COMPUTATIONAL METHODS FOR CONDITIONAL ESTIMATION
35
SMALL SAMPLE PROPERTIES OF PARAMETER ESTIMATES
59
ACCURACY AND COMPUTATIONAL EFFICIENCY OF MCMC
74
SUMMARY
84
APPENDIX A PROGRAMS FOR PARAMETER ESTIMATION FROM
94
clustexampmod c C program for gaining basic information prior
110
simc txt R program for generating random samples from a quadratic
143
CIFalse txt R program for calculating 1 α confidence intervals
157
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