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.