Analysis of familial aggregation using recurrence risk for complex survey data

Abstract

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, have 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.

Keywords:

• Recurrence risk
• network sampling
• extended quadratic exponential models
• diabetes

Acknowledgements

The opinion and information in this article are those of the authors and do not represent the views and/or policies of the US Food and Drug Administration. This work is part of Dr. Cong Wang's PhD thesis research conducted in the Department of Statistics at The George Washington University. The authors would like to thank the PhD committee and advisors who provided comments that greatly improved this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Cong Wang

Cong Wang, Ph.D., is a visiting scientist mathematical statistician in the Division of Biostatistics, Center of Biologics Evaluation and Research at the U.S. Food and Drug Administration. She received her Ph.D. degree in statistics at George Washington University.

Zhaohai Li

Zhaohai Li, Ph.D., is a professor of statistics in the Department of Statistics at George Washington University. He received his Ph.D. degree in statistics from Columbia University.

Barry I. Graubard

Barry Graubard, Ph.D., is a senior investigator in the Biostatistics Branch at the U.S. National Cancer Institute. He received his Ph.D. in mathematics from the University of Maryland at College Park, Md.