Abstract: Household data are frequently used in estimating vaccine efficacy because it provides information about every individual’s exposure to vaccinated and unvaccinated infected household members. This information is essential for reliable estimation of vaccine efficacy for infectiousness (V EI ), in addition to estimating vaccine efficacy for susceptibility (V ES ). However, accurate infection outcome data is not always available on each person due to high cost or lack of feasible methods to collect this information. Lack of reliable data on true infection status may result in biased or inefficient estimates of vaccine efficacy. In this paper, a semiparametric method that uses surrogate outcome data and a validation sample is introduced for estimation of V ES and V EI from a sample of households. The surrogate outcome data is usually based on illness symptoms. We report the results of simulations conducted to examine the performance of the estimates, compare the proposed semiparametric method with maximum likelihood methods that either use the validation data only or use the surrogate data only and address study design issues. The new method shows improved precision as compared to a method based on the validation sample only and smaller bias as compared to a method using surrogate outcome data only. In addition, the use of household data is shown to greatly improve the attenuation in the estimate of V ES due to misclassification of the outcome, as compared to the use of a random sample of unrelated individuals.
Abstract: Existing indices of observer agreement for continuous data, such as the intraclass correlation coefficient or the concordance correlation coefficient, measure the total observer-related variability, which includes the variabilities between and within observers. This work introduces a new index that measures the interobserver variability, which is defined in terms of the distances among the ‘true values’ assigned by different observers on the same subject. The new coefficient of interobserver variability (CIV ) is defined as the ratio of the interobserver and the total observer variability. We show how to estimate the CIV and how to use bootstrap and ANOVAbased methods for inference. We also develop a coefficient of excess observer variability, which compares the total observer variability to the expected total observer variability when there are no differences among the observers. This coefficient is a simple function of the CIV . In addition, we show how the value of the CIV , estimated from an agreement study, can be used in the design of measurements studies. We illustrate the new concepts and methods by two examples, where (1) two radiologists used calcium scores to evaluate the severity of coronary artery arteriosclerosis, and (2) two methods were used to measure knee joint angle.
Abstract: We propose a simple method for evaluating agreement between methods of measurement when the measured variable is continuous and the data consists of matched repeated observations made with the same method under different conditions. The conditions may represent different time points, raters, laboratories, treatments, etc. Our approach allows the values of the measured variable and the magnitude of disagreement to vary across the conditions. The coefficient of individual agreement (CIA), which is based on the comparison of the between and within-methods mean squared deviation (MSD) is used to quantify the magnitude of agreement between measurement methods. The new approach is illustrated via two examples from studies designed to compare (a) methods of evaluating carotid stenosis and (b) methods of measuring percent body fat.