Analysis of Familial Aggregation Using Recurrence Risk for Complex Survey Data

Familial or family aggregation of a disease is important for studying possible genetic etiology of a disease. A popular and useful measure of family aggregation is recurrence risk. Household health surveys with (family) network sampling, which surveyed individuals report about disease status of themselves and specified relatives, has been shown to be useful for estimating prevalence of diseases and more recently for estimating recurrence risk of disease using nonparametric classical survey methods. Because these surveys have complex sample designs with sample weighting for differential sample selection rates, this paper extends the composite-likelihood estimation and hypothesis of parameters of the quadratic exponential model (QEM) for simple random samples to data from these complex sample designs. In addition, the QEM is extended to simultaneously estimate and test parameters and recurrence risk for multiple family relationships, for comparing recurrence risk across family-level covariates (e.g., race) and utilizing propensity score weighting to adjust for confounding by individual-level covariates (e.g., age). Simulations are used to study the finite sample properties of the parameter estimation, variance estimation and level and power of hypothesis testing based on derived Wald and Quasi-Score tests for these extended QEMs. Finally, our methods are illustrated using the 1976 National Health Interview Survey diabetes data set.