title: Semi-parametric models for mismeasured exposure information in vaccine trials.
abstract: Exposure to infection information is important for estimating vaccine efficacy, but it is difficult to collect and inherently prone to missingness and mismeasurement. It is, therefore, generally not feasible to collect good exposure information on all participants in a large vaccine trial. We discuss study designs that collect detailed exposure information for only a small subset of trial participants, while collecting crude exposure information on all participants, and treat estimation of vaccine efficacy in the missing data/measurement error framework. We demonstrate with the example of an HIV vaccine trial the improvements in bias and efficiency when we combine the different levels of exposure information to estimate vaccine efficacy for reducing both susceptibility and infectiousness. We compare the performance of recently developed semi-parametric missing data methods of Pepe and Fleming and Carroll and Wand, Robins, Hsieh and Newey, and Reilly and Pepe.
authors.affiliation: Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
authors.forename: G T
authors.initials: GT
authors.lastname: Golm
authors.forename: M E
authors.initials: ME
authors.lastname: Halloran
authors.forename: I M
authors.initials: IM
authors.lastname: Longini
mesh_terms: D016915:AIDS Vaccines; D002986:Clinical Trials as Topic / Q000379:methods*; D004198:Disease Susceptibility; D015658:HIV Infections / Q000276:immunology / Q000517:prevention & control* / Q000635:transmission*; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D012307:Risk Factors; D012747:Sexual Partners
chemical_list: D016915:AIDS Vaccines