Bayesian hierarchical regression on clearance rates in the presence of "lag" and "tail" phases with an application to malaria parasites.

ABSTRACT: We present a principled technique for estimating the effect of covariates on malaria parasite clearance rates in the presence of "lag" and "tail" phases through the use of a Bayesian hierarchical linear model. 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 us 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. We use a changepoint model to account for both lag and tail phases, and hence

SUBMITTER: Fogarty CB 

PROVIDER: S-EPMC4575239 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature