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The coefficient of individual equivalence is a permutation-based measure of agreement between two observers making replicated readings on each subject. It compares the observed disagreement between the observers to the expected disagreement under individual equivalence. Individual equivalence of observers requires that for every study subject, the conditional distributions of the readings of the observers given the subject's characteristics are identical. Therefore, under individual equivalence it does not matter which observer is making a particular reading on a given subject. We introduce both nonparametric and parametric methods to estimate the coefficient as well as its standard error. We compare the new coefficient with the coefficient of individual agreement and with the concordance correlation coefficient. We also evaluate the performance of the estimates of the new coefficient via simulations and illustrate this new approach using data from a study comparing two noninvasive techniques for measuring carotid stenosis to an invasive gold standard., (Copyright © 2012 John Wiley & Sons, Ltd.)

A new coefficient for assessing agreement between two observers using a permutation-based method is introduced in this article. When observations are binary, this coefficient compares the observed disagreement (probability of discordance) between the observers with its expected value under the hypothesis of individual equivalence. This hypothesis states that for each subject, the conditional distributions of the readings of the two observers are identical, and therefore from a statistical viewpoint it does not matter which observer makes the reading on this subject. Let K and L denote the numbers of replicated observations that are available from observers X and Y, respectively, on a given subject. Then the expected disagreement under individual equivalence for a subject is based on the (K + L) choosing K possible assignments of X's and Y's to the K + L observations made on this subject. Simple methods for the estimation of the new coefficient and its standard error are derived. The new coefficient is compared with kappa and the coefficient of individual agreement, which is based on comparing the inter and intra observer disagreements. Simulation studies confirm the validity of the estimated coefficient and its standard error. Data from a study involving the evaluation of mammograms by 10 radiologists are used to illustrate this new approach to the evaluation of observer agreement., (Copyright © 2010 John Wiley & Sons, Ltd.)

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., (2004 John Wiley & Sons, Ltd.)

A good measure of field vaccine efficacy should evaluate the direct protective effect of vaccination on the person who receives the vaccine. The conventional estimator for vaccine efficacy depends on population level factors that are either unrelated or indirectly related to the direct biological action of the vaccine on persons, including population structure, duration of the study, the fraction vaccinated, and herd immunity, that is, indirect effects. Indirect effects can cause the conventional vaccine efficacy estimator to be inaccurate. We review alternative vaccine efficacy estimators that control for indirect effects at the population level. Thus, they are more accurate than the conventional estimator. We use epidemic simulations to explore the robustness of the conventional and proposed estimators under different field conditions. In addition, we apply the different vaccine efficacy estimators to data from a measles epidemic in Muyinga, Burundi.