Accommodation of additional non-randomly sampled cases in a study of Heliobacter pylori infection in families

Abstract

Epidemiological studies with two-stage designs typically gather information about some covariates from all study subjects in the first sampling stage, while additional data from only a subset of the subjects are collected in the second sampling stage. Appropriate analysis of two-stage studies maintains validity and can also improve precision. We describe an application of a weighted likelihood method, mean-score logistic regression, to accommodate data from a cross-sectional study of Helicobacter pylori infection in children, where the study sample was enriched with additional non-randomly sampled cases. The present work exemplifies how careful analysis of epidemiological data from complex sampling schemes can adjust for potential selection bias, improve precision and enable a more complete investigation of factors of interest. Our results highlight the importance of H. pylori infected mothers and siblings as risk factors for the infection in children in Sweden. Show more