Estimating vaccine efficacy from household data observed over time.
Estimation of vaccine efficacy has traditionally focused on the reduction in susceptibility to infection, or the vaccine efficacy for susceptibility (VE(S)). However, a vaccine, such as a prophylactic HIV vaccine, may also lower the infectiousness of a vaccinated person who became infected. The relative reduction in infectiousness due to vaccination is the vaccine efficacy for infectiousness (VE(I)). Estimation of VE(I) is challenging because it requires information on exposure to infection, and gathering this type of information is often expensive and difficult, or even impossible. Household studies are expected to provide more information on who is exposed to whom. In a previous paper, we developed a method for estimating VE(S) and VE(I) from a household study where only the final outbreak data are available. However, the resulting estimates were quite unstable. In this work, we develop a survival model for the estimation of VE(S) and VE(I) from household data where the time of infection is known for every study participant. Using stochastic simulations, we show that the proposed method significantly reduces the bias and mean square error in the estimation of both VE(S) and VE(I) as compared to the method based on final outbreak data. We also show that when time-to-event data are available, a household study produces more robust estimators than a same-size study of unrelated individuals. In addition, we investigate the bias in estimating VE(S) and VE(I) due to misclassification of infection status when only illness data, rather than true infection data, are available.