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.