title: A hierarchical Bayesian model to predict the duration of immunity to Haemophilus influenzae type b.
abstract: A hierarchical Bayesian regression model is fitted to longitudinal data on Haemophilus influenzae type b (Hib) serum antibodies. To estimate the decline rate of the antibody concentration, the model accommodates the possibility of unobserved subclinical infections with Hib bacteria that cause increasing concentrations during the study period. The computations rely on Markov chain Monte Carlo simulation of the joint posterior distribution of the model parameters. The model is used to predict the duration of immunity to subclinical Hib infection and to a serious invasive Hib disease.
authors: Rolf Nevanlinna Institute, University of Helsinki, Finland. kari.auranen@rni.helsinki.fi
authors: K
authors: K
authors: Auranen
authors: M
authors: M
authors: Eichner
authors: H
authors: H
authors: Käyhty
authors: A K
authors: AK
authors: Takala
authors: E
authors: E
authors: Arjas
mesh_terms: D000907:Antibodies, Bacterial / Q000097:blood; D001499:Bayes Theorem; D001699:Biometry; D002648:Child; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D005387:Finland; D006192:Haemophilus Infections / Q000276:immunology / Q000517:prevention & control; D018073:Haemophilus Vaccines / Q000494:pharmacology; D019741:Haemophilus influenzae type b / Q000276:immunology*; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012044:Regression Analysis
chemical_list: D000907:Antibodies, Bacterial; D018073:Haemophilus Vaccines