bhrcr-package: Bayesian Hierarchical Regression on Clearance Rates

In bhrcr: Bayesian Hierarchical Regression on Clearance Rates in the Presence of Lag and Tail Phases

Description

"bhrcr" provides tools for calculating, analyzing, and visualizing parasite clearance rates in the presence of "lag" and "tail" phases through the use of a Bayesian hierarchical linear model. The main function for the Bayesian hierarchical linear model is clearanceEstimatorBayes. Also the function calculatePCE performs the method presented in Flegg et al (2011). The hierarchical approach enables us to appropriately incorporate the uncertainty in both estimating clearance rates in patients and assessing the potential impact of covariates on these rates into the posterior intervals generated for the parameters associated with each covariate. Furthermore, it permits users to incorporate information about individuals for whom there exists only one observation time before censoring, which alleviates a systematic bias affecting inference when these individuals are excluded. The detailed model and simulation study are presented in the paper "Bayesian Hierarchical Regression on Clearance Rates in the Presence of Lag and Tail Phases with an Application to Malaria Parasites" by Fogarty et al. (2015).

References

Flegg, J. A., Guerin, P. J., White, N. J., & Stepniewska, K. (2011). Standardizing the measurement of parasite clearance in falciparum malaria: the parasite clearance estimator. Malaria journal, 10(1), 339.

Fogarty, C. B., Fay, M. P., Flegg, J. A., Stepniewska, K., Fairhurst, R. M., & Small, D. S. (2015). Bayesian hierarchical regression on clearance rates in the presence of "lag" and "tail" phases with an application to malaria parasites. Biometrics, 71(3), 751-759.

bhrcr documentation built on May 1, 2019, 8:41 p.m.