Biometrics

SummaryAdjusted attributable risk (AR) is the proportion of diseased individuals in a population that is due to an exposure. We consider estimates of adjusted AR based on odds ratios from logistic regression to adjust for confounding. Influence function methods used in survey sampling are applied to obtain simple and easily programmable expressions for estimating the variance of. These variance estimators can be applied to data from case–control, cross‐sectional, and cohort studies with or without frequency or individual matching and for sample designs with subject samples that range from simple random samples to (sample) weighted multistage stratified cluster samples like those used in national household surveys. The variance estimation ofis illustrated with: (i) a weighted stratified multistage clustered cross‐sectional study of childhood asthma from the Third National Health and Examination Survey (NHANES III), and (ii) a frequency‐matched case–control study of melanoma skin cancer.

Summary.A hierarchical Bayesian regression model is fitted to longitudinal data onHaemophilus influenzaetype b (Hib) serum antibodies. To estimate the decline rate of the antibody concentration, the model accommodates the possibility of unobserved subclinical infections with Hib bacteria that cause increasing concentrations during the study period. The computations rely on Markov chain Monte Carlo simulation of the joint posterior distribution of the model parameters. The model is used to predict the duration of immunity to subclinical Hib infection and to a serious invasive Hib disease.