Binary and count data naturally arise in clinical trials in health sciences. We consider a Bayesian analysis of binary and count data arising from two-arm clinical trials for testing hypotheses of equivalence.
For each type of data, we discuss the development of likelihood, the prior and the posterior distributions of parameters of interest. For binary data, we also examine the suitability of a normal approximation to the posterior distribution obtained via a Taylor series expansion.
When the posterior distribution is complex and high-dimensional, the Bayesian inference is carried out using Markov Chain Monte Carlo (MCMC) methods. We also discuss a meta-analysis approach for data arising from two-arm trials with multiple studies. We assign a Dirichlet process prior for the study effects parameters for accounting heterogeneity among multiple studies. We illustrate the methods using actual data arising from several health studies.
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/23588 |
| Date | 23 May 2014 |
| Creators | Kpekpena, Cynthia |
| Contributors | Muthukumarana, S (Statistics), Johnson, B (Statistics) Gumel, A(Mathematics) |
| Source Sets | University of Manitoba Canada |
| Detected Language | English |
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