This function calculates a 100*prob% credible interval for the F2 parameter using Bayesian methods. The model assumes a version of the Jerffreys' prior with a pooled variance-covariance matrix from based on the reference and test data sets. See Novick (2015) for more details of the model.
f2bayes(
dis_data,
prob = 0.9,
B = 1000,
ci.type = c("quantile", "HPD"),
get.dist = FALSE
)The function returns a 100*prob% credible interval for the F2 parameter calculated from the observed dissolution data.
A data frame containing the dissolution data. The first column of the data frame should denote
the group labels identifying whether a given dissolution belongs to the "reference" or "test" formulation group.
For a given dissolution run, the remaining columns of the data frame contains the individual run's dissolution
measurements sorted in time. Alternatively, the user may provide a data object of class dis_data containing the
dissolution data. See the make_dis_data() function for the particular structure of the data object.
The probability associated with the credible interval. A value between 0 and 1.
A positive integer specifying the number of Monte Carlo samples.
The type of credible interval to report. Specifying quantile returns a credible interval based on the posterior sample quantiles of the F2 distribution. Specifying HPD returns a highest posterior density interval.
logical; if TRUE, returns the posterior samples of the F2 distribution.
Novick, S., Shen, Y., Yang, H., Peterson, J., LeBlond, D., and Altan, S. (2015). Dissolution Curve Comparisons Through the F2 Parameter, a Bayesian Extension of the f2 Statistic. Journal of Biopharmaceutical Statistics, 25(2):351-371.
Pourmohamad, T., Oliva Aviles, C.M., and Richardson, R. Gaussian Process Modeling for Dissolution Curve Comparisons. Journal of the Royal Statistical Society, Series C, 71(2):331-351.
### dis_data comes loaded with the package
f2bayes(dis_data, prob = 0.9, B = 1000)
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