SEMIPARAMETRIC MODELS FOR MISMEASURED EXPOSURE INFORMATION IN VACCINETRIALS

Exposure to infection information is important for estimating vaccine efficacy, but it is difficult to collect and inherently prone to missi ngness and mismeasurement. It is, therefore, generally not feasible to collect good exposure information on all participants in a large vacc ine trial. We discuss study designs that collect detailed exposure inf ormation for only a small subset of trial participants, while collecti ng crude exposure information on all participants, and treat estimatio n of vaccine efficacy in the missing data/measurement error framework. We demonstrate with the example of an HIV vaccine trial the improveme nts in bias and efficiency when we combine the different levels of exp osure information to estimate vaccine efficacy for reducing both susce ptibility and infectiousness. We compare the performance of recently d eveloped semiparametric missing data methods of Pepe and Fleming and C arroll and Wand, Robins, Hsieh and Newey, and Reilly and Pepe. (C) 199 8 John Wiley & Sons, Ltd.