However, the results of considering managerial flexibility using the binomial model show that the uncertainty generates value for the project.
It has been used since the 1970s (Black and Scholes, 1973) to demonstrate that its results, under particular conditions (boundary conditions), are very similar to those obtained using the binomial model.
To model this distribution, we can use the binomial model.
In Table 2, we present different estimations of a pooled proportion (event rates) using the binomial model and the beta-binomial model.
Using the binomial model, we compute a binomial probability and variance as if all the data were from a single study with a sample size of over 3,000.
The number of bootstrap samples is M = 200 for IsoDE-Match and M = 20 for IsoDE-All, and bootstrap support was determined using the binomial model with significance level α = 0.05.
Using the binomial model when the variability in the data exceeds what the binomial model can accommodate could result in an underestimation of the standard error of the pooled event rate and thus increase the chance of a Type I error.
To estimate statistical significance of recurrent mutations across multiple datasets, we integrated the full set of reported mutations from these studies and used the binomial distribution to model the somatic mutation frequencies of genes.
Thus, our final model used the negative binomial model.
To examine the differences in the cumulative number of episodes of pneumonia, bronchitis, and bladder or kidney infection by HTLV status, we used the negative binomial model, a generalization of the Poisson model, to compute incidence rate ratios (RR) with 95% confidence interval (CI) for each of these outcomes.
In order to reduce the influence of missing data, we excluded pathways whose members were not significantly detected by GPL96 platform by using the binomial probability model.
