This analysis was undertaken using a binomial model in which the odds of the event (e.g., death) are calculated for each study and study arm level information is incorporated in the model.
Since Bayesian statistical methods are now increasingly used in clinical and public health research, we hereby briefly describe the derivation of the beta-binomial model in the Bayesian framework.
We ran the binomial model in WinBUGS 1.4.1 [ 33] to derive, from the marginal posterior distributions of our parameters of interest, medians, standard deviations and 95% credible intervals.
The important issue naturally is the test of overdispersion since that is the basis for preferring the beta-binomial model in these situations.
The data were then modeled using a Negative Binomial model in edgeR to identify differentially expressed genes after inoculation using the dispersion estimated from the housekeeping genes as a common dispersion.
We modeled each putative synapse forming a connection as a theoretical, "average" synapse, whose properties were the mean of all the synapses forming the same connection, equivalent to a simple binomial model in quantal analysis.
Using a new binomial model in conjunction with a mouse model with identifiable alleles and skewed X inactivation we are able to survey genes that escape XCI in vivo.
Table 4 shows the RR of country of origin, sex, and age determined through the negative binomial models in all the specialties.
We formulated multiple variable Poisson and negative binomial models for each healthcare utilization category; however, in the likelihood ratio test of over-dispersion the alphas were significantly different from zero, which led us to reject the Poisson models for the negative binomial models in every case.
We tested for differential expression of transcripts in larvae reared on the two Arabidopsis lines by first summing the reads mapped across all contigs in each transcript to generate a count of reads mapped to each transcript in each pool, and estimating log2-fold change per treatment (WT GKO) using a negative binomial model implemented in the R/Bioconductor package DESeq (Anders and Huber 2010).
